From 412ccfc1806a0319947e27e06d483c45098c7002 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Hynek=20Kydl=C3=AD=C4=8Dek?= Date: Mon, 9 Dec 2024 18:56:07 +0100 Subject: [PATCH 01/12] Adds Global MLMU (#426) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add global mmlu + zulu * add global mmlu + zulu * fix translatin literals * add unk for global mmlu * Update src/lighteval/tasks/multilingual/tasks.py Co-authored-by: Clémentine Fourrier <22726840+clefourrier@users.noreply.github.com> --------- Co-authored-by: Clémentine Fourrier <22726840+clefourrier@users.noreply.github.com> --- src/lighteval/tasks/multilingual/tasks.py | 87 +++++++++++++++++++ .../templates/utils/translation_literals.py | 1 + src/lighteval/utils/language.py | 1 + 3 files changed, 89 insertions(+) diff --git a/src/lighteval/tasks/multilingual/tasks.py b/src/lighteval/tasks/multilingual/tasks.py index c788871ba..93d8cea40 100644 --- a/src/lighteval/tasks/multilingual/tasks.py +++ b/src/lighteval/tasks/multilingual/tasks.py @@ -1707,6 +1707,92 @@ ] ] +# Translated MMLU using both professional and non-professional translators. Contains tags for cultural sensitivity. +# CA: Cultural Agnostic +# CS: Cultural Specific +# UNK: Not annotated +# ALL: All of the above +# https://huggingface.co/papers/2412.03304 +global_mmlu_tasks = [ + LightevalTaskConfig( + name=f"global_mmlu_{sensitivity_label.lower()}_{language.value}_{formulation.name.lower()}:{subset}", + prompt_function=get_mcq_prompt_function( + language, + lambda line: { + "question": line["question"], + "choices": [line["option_a"], line["option_b"], line["option_c"], line["option_d"]], + "gold_idx": LETTER_INDICES.index(line["answer"]), + }, + formulation=formulation, + ), + suite=("lighteval",), + hf_repo="CohereForAI/Global-MMLU", + hf_subset=standardize_tag(language.value), + evaluation_splits=("test",), + few_shots_split="dev", + hf_filter=partial( + lambda subset, sensitivity_label, x: x["subject"].lower() == subset + and ( + sensitivity_label == "ALL" or sensitivity_label in x["cultural_sensitivity_label"].replace("-", "UNK") + ), + subset, + sensitivity_label, + ), + metric=get_metrics_for_formulation( + formulation, + [ + loglikelihood_acc_metric(normalization=LogProbTokenNorm()), + loglikelihood_acc_metric(normalization=LogProbCharNorm()), + loglikelihood_acc_metric(normalization=LogProbPMINorm()), + ], + ), + ) + for subset in MMLU_SUBSETS + for language in [ + Language.AMHARIC, + Language.ARABIC, + Language.BENGALI, + Language.CHINESE, + Language.CZECH, + Language.GERMAN, + Language.ENGLISH, + Language.SPANISH, + Language.FRENCH, + Language.HEBREW, + Language.HINDI, + Language.INDONESIAN, + Language.ITALIAN, + Language.JAPANESE, + Language.KOREAN, + Language.MALAY, + Language.DUTCH, + Language.NORWEGIAN, + Language.POLISH, + Language.PORTUGUESE, + Language.ROMANIAN, + Language.RUSSIAN, + Language.SERBIAN, + Language.SWEDISH, + Language.SWAHILI, + Language.TAMIL, + Language.TELUGU, + Language.THAI, + Language.TURKISH, + Language.UKRAINIAN, + Language.URDU, + Language.VIETNAMESE, + Language.YORUBA, + Language.ZULU, + ] + for formulation in [ + MCFFormulation(), + CFFormulation(), + HybridFormulation(), + ] + for sensitivity_label in ["ALL", "CA", "CS", "UNK"] +] + + # There are only these subsets in the African MMLU AFRI_MMLU_SUBSETS = [ "elementary_mathematics", @@ -2088,6 +2174,7 @@ *arabic_mmlu_tasks, *turkish_mmlu_tasks, *afri_mmlu_tasks, + *global_mmlu_tasks, ] ) diff --git a/src/lighteval/tasks/templates/utils/translation_literals.py b/src/lighteval/tasks/templates/utils/translation_literals.py index 186d64485..441b5a7b6 100644 --- a/src/lighteval/tasks/templates/utils/translation_literals.py +++ b/src/lighteval/tasks/templates/utils/translation_literals.py @@ -1007,4 +1007,5 @@ def __getattribute__(self, name: str) -> str: Language.WESTERN_FRISIAN: TranslationLiterals(language=Language.WESTERN_FRISIAN), Language.YIDDISH: TranslationLiterals(language=Language.YIDDISH), Language.YORUBA: TranslationLiterals(language=Language.YORUBA), + Language.ZULU: TranslationLiterals(language=Language.ZULU), } diff --git a/src/lighteval/utils/language.py b/src/lighteval/utils/language.py index e6e53984e..d59908b01 100644 --- a/src/lighteval/utils/language.py +++ b/src/lighteval/utils/language.py @@ -122,6 +122,7 @@ class Language(Enum): WAR = "war" SHAN = "shn" UDMURT = "udm" + ZULU = "zul" # This mapping was created for beleble, it converts iso_639_3 individual codes to iso_639_3 macro codes From 9315f0d4d893050fce4e8b30839330052f4d15d5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cl=C3=A9mentine=20Fourrier?= <22726840+clefourrier@users.noreply.github.com> Date: Tue, 10 Dec 2024 14:49:19 +0100 Subject: [PATCH 02/12] add configs with their models (#421) * add configs with their models * fix tests * doc update * doc update * fix path --- docs/source/_toctree.yml | 4 +- .../source/package_reference/model_config.mdx | 10 - docs/source/package_reference/models.mdx | 32 +- src/lighteval/main_accelerate.py | 4 +- src/lighteval/main_endpoint.py | 2 +- src/lighteval/main_vllm.py | 2 +- .../models/{ => dummy}/dummy_model.py | 7 +- .../models/{ => endpoints}/endpoint_model.py | 57 +++- .../models/{ => endpoints}/openai_model.py | 8 +- .../models/{ => endpoints}/tgi_model.py | 10 +- src/lighteval/models/model_config.py | 314 ------------------ src/lighteval/models/model_loader.py | 25 +- .../models/{ => nanotron}/nanotron_model.py | 2 +- .../{ => transformers}/adapter_model.py | 24 +- .../models/{ => transformers}/base_model.py | 162 ++++++++- .../models/{ => transformers}/delta_model.py | 23 +- src/lighteval/models/{ => vllm}/vllm_model.py | 26 +- src/lighteval/tasks/lighteval_task.py | 2 +- tests/models/test_abstract_model.py | 3 +- tests/models/test_base_model.py | 3 +- 20 files changed, 342 insertions(+), 378 deletions(-) delete mode 100644 docs/source/package_reference/model_config.mdx rename src/lighteval/models/{ => dummy}/dummy_model.py (97%) rename src/lighteval/models/{ => endpoints}/endpoint_model.py (91%) rename src/lighteval/models/{ => endpoints}/openai_model.py (98%) rename src/lighteval/models/{ => endpoints}/tgi_model.py (94%) delete mode 100644 src/lighteval/models/model_config.py rename src/lighteval/models/{ => nanotron}/nanotron_model.py (99%) rename src/lighteval/models/{ => transformers}/adapter_model.py (80%) rename src/lighteval/models/{ => transformers}/base_model.py (87%) rename src/lighteval/models/{ => transformers}/delta_model.py (81%) rename src/lighteval/models/{ => vllm}/vllm_model.py (92%) diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml index 3daa40529..9ad55466a 100644 --- a/docs/source/_toctree.yml +++ b/docs/source/_toctree.yml @@ -33,9 +33,7 @@ - local: package_reference/evaluation_tracker title: EvaluationTracker - local: package_reference/models - title: Models - - local: package_reference/model_config - title: ModelConfig + title: Models and ModelConfigs - local: package_reference/pipeline title: Pipeline title: Main classes diff --git a/docs/source/package_reference/model_config.mdx b/docs/source/package_reference/model_config.mdx deleted file mode 100644 index e2ecceb4d..000000000 --- a/docs/source/package_reference/model_config.mdx +++ /dev/null @@ -1,10 +0,0 @@ -# ModelConfig - -[[autodoc]] models.model_config.BaseModelConfig - -[[autodoc]] models.model_config.AdapterModelConfig -[[autodoc]] models.model_config.DeltaModelConfig -[[autodoc]] models.model_config.InferenceEndpointModelConfig -[[autodoc]] models.model_config.InferenceModelConfig -[[autodoc]] models.model_config.TGIModelConfig -[[autodoc]] models.model_config.VLLMModelConfig diff --git a/docs/source/package_reference/models.mdx b/docs/source/package_reference/models.mdx index 34b5b2739..096ce7be3 100644 --- a/docs/source/package_reference/models.mdx +++ b/docs/source/package_reference/models.mdx @@ -4,24 +4,38 @@ ### LightevalModel [[autodoc]] models.abstract_model.LightevalModel + ## Accelerate and Transformers Models ### BaseModel -[[autodoc]] models.base_model.BaseModel +[[autodoc]] models.transformers.base_model.BaseModelConfig +[[autodoc]] models.transformers.base_model.BaseModel + ### AdapterModel -[[autodoc]] models.adapter_model.AdapterModel +[[autodoc]] models.transformers.adapter_model.AdapterModelConfig +[[autodoc]] models.transformers.adapter_model.AdapterModel + ### DeltaModel -[[autodoc]] models.delta_model.DeltaModel +[[autodoc]] models.transformers.delta_model.DeltaModelConfig +[[autodoc]] models.transformers.delta_model.DeltaModel -## Inference Endpoints and TGI Models +## Endpoints-based Models ### InferenceEndpointModel -[[autodoc]] models.endpoint_model.InferenceEndpointModel -### ModelClient -[[autodoc]] models.tgi_model.ModelClient +[[autodoc]] models.endpoints.endpoint_model.InferenceEndpointModelConfig +[[autodoc]] models.endpoints.endpoint_model.InferenceModelConfig +[[autodoc]] models.endpoints.endpoint_model.InferenceEndpointModel + +### TGI ModelClient +[[autodoc]] models.endpoints.tgi_model.TGIModelConfig +[[autodoc]] models.endpoints.tgi_model.ModelClient + +### Open AI Models +[[autodoc]] models.endpoints.openai_model.OpenAIClient ## Nanotron Model ### NanotronLightevalModel -[[autodoc]] models.nanotron_model.NanotronLightevalModel +[[autodoc]] models.nanotron.nanotron_model.NanotronLightevalModel ## VLLM Model ### VLLMModel -[[autodoc]] models.vllm_model.VLLMModel +[[autodoc]] models.vllm.vllm_model.VLLMModelConfig +[[autodoc]] models.vllm.vllm_model.VLLMModel diff --git a/src/lighteval/main_accelerate.py b/src/lighteval/main_accelerate.py index e7d18c809..27e4141f5 100644 --- a/src/lighteval/main_accelerate.py +++ b/src/lighteval/main_accelerate.py @@ -107,7 +107,9 @@ def accelerate( # noqa C901 from accelerate import Accelerator, InitProcessGroupKwargs from lighteval.logging.evaluation_tracker import EvaluationTracker - from lighteval.models.model_config import AdapterModelConfig, BaseModelConfig, BitsAndBytesConfig, DeltaModelConfig + from lighteval.models.transformers.adapter_model import AdapterModelConfig + from lighteval.models.transformers.base_model import BaseModelConfig, BitsAndBytesConfig + from lighteval.models.transformers.delta_model import DeltaModelConfig from lighteval.pipeline import EnvConfig, ParallelismManager, Pipeline, PipelineParameters accelerator = Accelerator(kwargs_handlers=[InitProcessGroupKwargs(timeout=timedelta(seconds=3000))]) diff --git a/src/lighteval/main_endpoint.py b/src/lighteval/main_endpoint.py index 5069c414f..d17da4325 100644 --- a/src/lighteval/main_endpoint.py +++ b/src/lighteval/main_endpoint.py @@ -201,7 +201,7 @@ def inference_endpoint( import yaml from lighteval.logging.evaluation_tracker import EvaluationTracker - from lighteval.models.model_config import ( + from lighteval.models.endpoints.endpoint_model import ( InferenceEndpointModelConfig, ) from lighteval.pipeline import EnvConfig, ParallelismManager, Pipeline, PipelineParameters diff --git a/src/lighteval/main_vllm.py b/src/lighteval/main_vllm.py index 4bd1681d0..078000da5 100644 --- a/src/lighteval/main_vllm.py +++ b/src/lighteval/main_vllm.py @@ -89,7 +89,7 @@ def vllm( Evaluate models using vllm as backend. """ from lighteval.logging.evaluation_tracker import EvaluationTracker - from lighteval.models.model_config import VLLMModelConfig + from lighteval.models.vllm.vllm_model import VLLMModelConfig from lighteval.pipeline import EnvConfig, ParallelismManager, Pipeline, PipelineParameters TOKEN = os.getenv("HF_TOKEN") diff --git a/src/lighteval/models/dummy_model.py b/src/lighteval/models/dummy/dummy_model.py similarity index 97% rename from src/lighteval/models/dummy_model.py rename to src/lighteval/models/dummy/dummy_model.py index b9fa60e0d..ff89656be 100644 --- a/src/lighteval/models/dummy_model.py +++ b/src/lighteval/models/dummy/dummy_model.py @@ -23,12 +23,12 @@ # inspired by https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/models/dummy.py import random +from dataclasses import dataclass from typing import Optional from transformers import AutoTokenizer from lighteval.models.abstract_model import LightevalModel, ModelInfo -from lighteval.models.model_config import DummyModelConfig from lighteval.models.model_output import GenerativeResponse, LoglikelihoodResponse, LoglikelihoodSingleTokenResponse from lighteval.tasks.requests import ( GreedyUntilRequest, @@ -39,6 +39,11 @@ from lighteval.utils.utils import EnvConfig +@dataclass +class DummyModelConfig: + seed: int = 42 + + class DummyModel(LightevalModel): """Dummy model to generate random baselines.""" diff --git a/src/lighteval/models/endpoint_model.py b/src/lighteval/models/endpoints/endpoint_model.py similarity index 91% rename from src/lighteval/models/endpoint_model.py rename to src/lighteval/models/endpoints/endpoint_model.py index bd82f058a..112338964 100644 --- a/src/lighteval/models/endpoint_model.py +++ b/src/lighteval/models/endpoints/endpoint_model.py @@ -24,7 +24,8 @@ import logging import re import time -from typing import Coroutine, List, Optional, Union +from dataclasses import dataclass +from typing import Coroutine, Dict, List, Optional, Union import requests import torch @@ -47,7 +48,6 @@ from lighteval.data import GenerativeTaskDataset, LoglikelihoodDataset from lighteval.models.abstract_model import LightevalModel, ModelInfo -from lighteval.models.model_config import InferenceEndpointModelConfig, InferenceModelConfig from lighteval.models.model_output import GenerativeResponse, LoglikelihoodResponse, LoglikelihoodSingleTokenResponse from lighteval.tasks.requests import ( GreedyUntilRequest, @@ -74,6 +74,59 @@ ] +@dataclass +class InferenceModelConfig: + model: str + add_special_tokens: bool = True + + +@dataclass +class InferenceEndpointModelConfig: + endpoint_name: str = None + model_name: str = None + should_reuse_existing: bool = False + accelerator: str = "gpu" + model_dtype: str = None # if empty, we use the default + vendor: str = "aws" + region: str = "us-east-1" # this region has the most hardware options available + instance_size: str = None # if none, we autoscale + instance_type: str = None # if none, we autoscale + framework: str = "pytorch" + endpoint_type: str = "protected" + add_special_tokens: bool = True + revision: str = "main" + namespace: str = None # The namespace under which to launch the endopint. Defaults to the current user's namespace + image_url: str = None + env_vars: dict = None + + def __post_init__(self): + # xor operator, one is None but not the other + if (self.instance_size is None) ^ (self.instance_type is None): + raise ValueError( + "When creating an inference endpoint, you need to specify explicitely both instance_type and instance_size, or none of them for autoscaling." + ) + + if not (self.endpoint_name is None) ^ int(self.model_name is None): + raise ValueError("You need to set either endpoint_name or model_name (but not both).") + + def get_dtype_args(self) -> Dict[str, str]: + if self.model_dtype is None: + return {} + model_dtype = self.model_dtype.lower() + if model_dtype in ["awq", "eetq", "gptq"]: + return {"QUANTIZE": model_dtype} + if model_dtype == "8bit": + return {"QUANTIZE": "bitsandbytes"} + if model_dtype == "4bit": + return {"QUANTIZE": "bitsandbytes-nf4"} + if model_dtype in ["bfloat16", "float16"]: + return {"DTYPE": model_dtype} + return {} + + def get_custom_env_vars(self) -> Dict[str, str]: + return {k: str(v) for k, v in self.env_vars.items()} if self.env_vars else {} + + class InferenceEndpointModel(LightevalModel): """InferenceEndpointModels can be used both with the free inference client, or with inference endpoints, which will use text-generation-inference to deploy your model for the duration of the evaluation. diff --git a/src/lighteval/models/openai_model.py b/src/lighteval/models/endpoints/openai_model.py similarity index 98% rename from src/lighteval/models/openai_model.py rename to src/lighteval/models/endpoints/openai_model.py index 12fbeb95c..b2ca25285 100644 --- a/src/lighteval/models/openai_model.py +++ b/src/lighteval/models/endpoints/openai_model.py @@ -24,13 +24,14 @@ import os import time from concurrent.futures import ThreadPoolExecutor +from dataclasses import dataclass from typing import Optional from tqdm import tqdm from lighteval.data import GenerativeTaskDataset, LoglikelihoodDataset from lighteval.models.abstract_model import LightevalModel -from lighteval.models.endpoint_model import ModelInfo +from lighteval.models.endpoints.endpoint_model import ModelInfo from lighteval.models.model_output import ( GenerativeResponse, LoglikelihoodResponse, @@ -58,6 +59,11 @@ logging.getLogger("httpx").setLevel(logging.ERROR) +@dataclass +class OpenAIModelConfig: + model: str + + class OpenAIClient(LightevalModel): _DEFAULT_MAX_LENGTH: int = 4096 diff --git a/src/lighteval/models/tgi_model.py b/src/lighteval/models/endpoints/tgi_model.py similarity index 94% rename from src/lighteval/models/tgi_model.py rename to src/lighteval/models/endpoints/tgi_model.py index 99d7bd100..d95609a50 100644 --- a/src/lighteval/models/tgi_model.py +++ b/src/lighteval/models/endpoints/tgi_model.py @@ -21,13 +21,14 @@ # SOFTWARE. import asyncio +from dataclasses import dataclass from typing import Coroutine, Optional import requests from huggingface_hub import TextGenerationInputGrammarType, TextGenerationOutput from transformers import AutoTokenizer -from lighteval.models.endpoint_model import InferenceEndpointModel, ModelInfo +from lighteval.models.endpoints.endpoint_model import InferenceEndpointModel, ModelInfo from lighteval.utils.imports import NO_TGI_ERROR_MSG, is_tgi_available @@ -44,6 +45,13 @@ def divide_chunks(array, n): yield array[i : i + n] +@dataclass +class TGIModelConfig: + inference_server_address: str + inference_server_auth: str + model_id: str + + # inherit from InferenceEndpointModel instead of LightevalModel since they both use the same interface, and only overwrite # the client functions, since they use a different client. class ModelClient(InferenceEndpointModel): diff --git a/src/lighteval/models/model_config.py b/src/lighteval/models/model_config.py deleted file mode 100644 index 1eda1e029..000000000 --- a/src/lighteval/models/model_config.py +++ /dev/null @@ -1,314 +0,0 @@ -# MIT License - -# Copyright (c) 2024 The HuggingFace Team - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -# SOFTWARE. - -import logging -from dataclasses import dataclass -from typing import Dict, Optional, Union - -import torch -from transformers import AutoConfig, BitsAndBytesConfig, GPTQConfig, PretrainedConfig - -from lighteval.models.utils import _get_model_sha -from lighteval.utils.imports import ( - NO_AUTOGPTQ_ERROR_MSG, - NO_BNB_ERROR_MSG, - NO_PEFT_ERROR_MSG, - is_accelerate_available, - is_autogptq_available, - is_bnb_available, - is_peft_available, -) -from lighteval.utils.utils import EnvConfig, boolstring_to_bool - - -logger = logging.getLogger(__name__) - -if is_accelerate_available(): - from accelerate import Accelerator - - -@dataclass -class BaseModelConfig: - """ - Base configuration class for models. - - Attributes: - pretrained (str): - HuggingFace Hub model ID name or the path to a pre-trained - model to load. This is effectively the `pretrained_model_name_or_path` - argument of `from_pretrained` in the HuggingFace `transformers` API. - accelerator (Accelerator): accelerator to use for model training. - tokenizer (Optional[str]): HuggingFace Hub tokenizer ID that will be - used for tokenization. - multichoice_continuations_start_space (Optional[bool]): Whether to add a - space at the start of each continuation in multichoice generation. - For example, context: "What is the capital of France?" and choices: "Paris", "London". - Will be tokenized as: "What is the capital of France? Paris" and "What is the capital of France? London". - True adds a space, False strips a space, None does nothing - pairwise_tokenization (bool): Whether to tokenize the context and continuation as separately or together. - subfolder (Optional[str]): The subfolder within the model repository. - revision (str): The revision of the model. - batch_size (int): The batch size for model training. - max_gen_toks (Optional[int]): The maximum number of tokens to generate. - max_length (Optional[int]): The maximum length of the generated output. - add_special_tokens (bool, optional, defaults to True): Whether to add special tokens to the input sequences. - If `None`, the default value will be set to `True` for seq2seq models (e.g. T5) and - `False` for causal models. - model_parallel (bool, optional, defaults to False): - True/False: force to use or not the `accelerate` library to load a large - model across multiple devices. - Default: None which corresponds to comparing the number of processes with - the number of GPUs. If it's smaller => model-parallelism, else not. - dtype (Union[str, torch.dtype], optional, defaults to None):): - Converts the model weights to `dtype`, if specified. Strings get - converted to `torch.dtype` objects (e.g. `float16` -> `torch.float16`). - Use `dtype="auto"` to derive the type from the model's weights. - device (Union[int, str]): device to use for model training. - quantization_config (Optional[BitsAndBytesConfig]): quantization - configuration for the model, manually provided to load a normally floating point - model at a quantized precision. Needed for 4-bit and 8-bit precision. - trust_remote_code (bool): Whether to trust remote code during model - loading. - - Methods: - __post_init__(): Performs post-initialization checks on the configuration. - _init_configs(model_name, env_config): Initializes the model configuration. - init_configs(env_config): Initializes the model configuration using the environment configuration. - get_model_sha(): Retrieves the SHA of the model. - - """ - - pretrained: str - accelerator: "Accelerator" = None - tokenizer: Optional[str] = None - multichoice_continuations_start_space: Optional[bool] = None - pairwise_tokenization: bool = False - subfolder: Optional[str] = None - revision: str = "main" - batch_size: int = -1 - max_gen_toks: Optional[int] = 256 - max_length: Optional[int] = None - add_special_tokens: bool = True - model_parallel: Optional[bool] = None - dtype: Optional[Union[str, torch.dtype]] = None - device: Union[int, str] = "cuda" - quantization_config: Optional[BitsAndBytesConfig] = None - trust_remote_code: bool = False - use_chat_template: bool = False - compile: bool = False - - def __post_init__(self): - # Making sure this parameter is a boolean - self.multichoice_continuations_start_space = boolstring_to_bool(self.multichoice_continuations_start_space) - - if self.multichoice_continuations_start_space is not None: - if self.multichoice_continuations_start_space: - logger.info( - "You set `multichoice_continuations_start_space` to true. This will force multichoice continuations to use a starting space" - ) - else: - logger.info( - "You set `multichoice_continuations_start_space` to false. This will remove a leading space from multichoice continuations, if present." - ) - - self.model_parallel = boolstring_to_bool(self.model_parallel) - self.compile = boolstring_to_bool(self.compile) - - if self.quantization_config is not None and not is_bnb_available(): - raise ImportError(NO_BNB_ERROR_MSG) - - if not isinstance(self.pretrained, str): - raise ValueError("Pretrained model name must be passed as string.") - if not isinstance(self.device, str): - raise ValueError("Current device must be passed as string.") - - def _init_configs(self, model_name: str, env_config: EnvConfig) -> PretrainedConfig: - revision = self.revision - if self.subfolder: - revision = f"{self.revision}/{self.subfolder}" - auto_config = AutoConfig.from_pretrained( - model_name, - revision=revision, - trust_remote_code=self.trust_remote_code, - cache_dir=env_config.cache_dir, - token=env_config.token, - ) - - # Gathering the model's automatic quantization config, if available - try: - model_auto_quantization_config = auto_config.quantization_config - logger.info("An automatic quantization config was found in the model's config. Using it to load the model") - except (AttributeError, KeyError): - model_auto_quantization_config = None - - if model_auto_quantization_config is not None: - if self.quantization_config is not None: - # We don't load models quantized by default with a different user provided conf - raise ValueError("You manually requested quantization on a model already quantized!") - - # We add the quantization to the model params we store - if model_auto_quantization_config["quant_method"] == "gptq": - if not is_autogptq_available(): - raise ImportError(NO_AUTOGPTQ_ERROR_MSG) - auto_config.quantization_config["use_exllama"] = None - self.quantization_config = GPTQConfig(**auto_config.quantization_config, disable_exllama=True) - elif model_auto_quantization_config["quant_method"] == "bitsandbytes": - if not is_bnb_available(): - raise ImportError(NO_BNB_ERROR_MSG) - self.quantization_config = BitsAndBytesConfig(**auto_config.quantization_config) - - return auto_config - - def init_configs(self, env_config: EnvConfig) -> PretrainedConfig: - return self._init_configs(self.pretrained, env_config=env_config) - - def get_model_sha(self): - return _get_model_sha(repo_id=self.pretrained, revision=self.revision) - - -@dataclass -class DeltaModelConfig(BaseModelConfig): - # Delta models look at the pretrained (= the delta weights) for the tokenizer and model config - base_model: str = None - - def __post_init__(self): - self.revision = "main" - - if not self.base_model: # must have a default value bc of dataclass inheritance, but can't actually be None - raise ValueError("The base_model argument must not be null for a delta model config") - - return super().__post_init__() - - def get_model_sha(self): - return _get_model_sha(repo_id=self.pretrained, revision="main") - - -@dataclass -class AdapterModelConfig(BaseModelConfig): - # Adapter models have the specificity that they look at the base model (= the parent) for the tokenizer and config - base_model: str = None - - def __post_init__(self): - if not is_peft_available(): - raise ImportError(NO_PEFT_ERROR_MSG) - - if not self.base_model: # must have a default value bc of dataclass inheritance, but can't actually be None - raise ValueError("The base_model argument must not be null for an adapter model config") - - return super().__post_init__() - - def init_configs(self, env_config: EnvConfig): - return self._init_configs(self.base_model, env_config) - - -@dataclass -class VLLMModelConfig: - pretrained: str - gpu_memory_utilisation: float = 0.9 # lower this if you are running out of memory - revision: str = "main" # revision of the model - dtype: str | None = None - tensor_parallel_size: int = 1 # how many GPUs to use for tensor parallelism - pipeline_parallel_size: int = 1 # how many GPUs to use for pipeline parallelism - data_parallel_size: int = 1 # how many GPUs to use for data parallelism - max_model_length: int | None = None # maximum length of the model, ussually infered automatically. reduce this if you encouter OOM issues, 4096 is usually enough - swap_space: int = 4 # CPU swap space size (GiB) per GPU. - seed: int = 1234 - trust_remote_code: bool = False - use_chat_template: bool = False - add_special_tokens: bool = True - multichoice_continuations_start_space: bool = ( - True # whether to add a space at the start of each continuation in multichoice generation - ) - pairwise_tokenization: bool = False # whether to tokenize the context and continuation separately or together. - - subfolder: Optional[str] = None - temperature: float = 0.6 # will be used for multi sampling tasks, for tasks requiring no sampling, this will be ignored and set to 0. - - -@dataclass -class OpenAIModelConfig: - model: str - - -@dataclass -class TGIModelConfig: - inference_server_address: str - inference_server_auth: str - model_id: str - - -@dataclass -class DummyModelConfig: - seed: int = 42 - - -@dataclass -class InferenceModelConfig: - model: str - add_special_tokens: bool = True - - -@dataclass -class InferenceEndpointModelConfig: - endpoint_name: str = None - model_name: str = None - should_reuse_existing: bool = False - accelerator: str = "gpu" - model_dtype: str = None # if empty, we use the default - vendor: str = "aws" - region: str = "us-east-1" # this region has the most hardware options available - instance_size: str = None # if none, we autoscale - instance_type: str = None # if none, we autoscale - framework: str = "pytorch" - endpoint_type: str = "protected" - add_special_tokens: bool = True - revision: str = "main" - namespace: str = None # The namespace under which to launch the endopint. Defaults to the current user's namespace - image_url: str = None - env_vars: dict = None - - def __post_init__(self): - # xor operator, one is None but not the other - if (self.instance_size is None) ^ (self.instance_type is None): - raise ValueError( - "When creating an inference endpoint, you need to specify explicitely both instance_type and instance_size, or none of them for autoscaling." - ) - - if not (self.endpoint_name is None) ^ int(self.model_name is None): - raise ValueError("You need to set either endpoint_name or model_name (but not both).") - - def get_dtype_args(self) -> Dict[str, str]: - if self.model_dtype is None: - return {} - model_dtype = self.model_dtype.lower() - if model_dtype in ["awq", "eetq", "gptq"]: - return {"QUANTIZE": model_dtype} - if model_dtype == "8bit": - return {"QUANTIZE": "bitsandbytes"} - if model_dtype == "4bit": - return {"QUANTIZE": "bitsandbytes-nf4"} - if model_dtype in ["bfloat16", "float16"]: - return {"DTYPE": model_dtype} - return {} - - def get_custom_env_vars(self) -> Dict[str, str]: - return {k: str(v) for k, v in self.env_vars.items()} if self.env_vars else {} diff --git a/src/lighteval/models/model_loader.py b/src/lighteval/models/model_loader.py index 1a409746c..b0817be4a 100644 --- a/src/lighteval/models/model_loader.py +++ b/src/lighteval/models/model_loader.py @@ -23,25 +23,18 @@ import logging from typing import Union -from lighteval.models.adapter_model import AdapterModel -from lighteval.models.base_model import BaseModel -from lighteval.models.delta_model import DeltaModel -from lighteval.models.dummy_model import DummyModel -from lighteval.models.endpoint_model import InferenceEndpointModel -from lighteval.models.model_config import ( - AdapterModelConfig, - BaseModelConfig, - DeltaModelConfig, - DummyModelConfig, +from lighteval.models.dummy.dummy_model import DummyModel, DummyModelConfig +from lighteval.models.endpoints.endpoint_model import ( + InferenceEndpointModel, InferenceEndpointModelConfig, InferenceModelConfig, - OpenAIModelConfig, - TGIModelConfig, - VLLMModelConfig, ) -from lighteval.models.openai_model import OpenAIClient -from lighteval.models.tgi_model import ModelClient -from lighteval.models.vllm_model import VLLMModel +from lighteval.models.endpoints.openai_model import OpenAIClient, OpenAIModelConfig +from lighteval.models.endpoints.tgi_model import ModelClient, TGIModelConfig +from lighteval.models.transformers.adapter_model import AdapterModel, AdapterModelConfig +from lighteval.models.transformers.base_model import BaseModel, BaseModelConfig +from lighteval.models.transformers.delta_model import DeltaModel, DeltaModelConfig +from lighteval.models.vllm.vllm_model import VLLMModel, VLLMModelConfig from lighteval.utils.imports import ( NO_TGI_ERROR_MSG, NO_VLLM_ERROR_MSG, diff --git a/src/lighteval/models/nanotron_model.py b/src/lighteval/models/nanotron/nanotron_model.py similarity index 99% rename from src/lighteval/models/nanotron_model.py rename to src/lighteval/models/nanotron/nanotron_model.py index 21b605040..b7e9b1a5d 100644 --- a/src/lighteval/models/nanotron_model.py +++ b/src/lighteval/models/nanotron/nanotron_model.py @@ -42,13 +42,13 @@ LoglikelihoodDataset, LoglikelihoodSingleTokenDataset, ) -from lighteval.models.base_model import LightevalModel, ModelInfo from lighteval.models.model_output import ( Batch, GenerativeResponse, LoglikelihoodResponse, LoglikelihoodSingleTokenResponse, ) +from lighteval.models.transformers.base_model import LightevalModel, ModelInfo from lighteval.tasks.requests import ( GreedyUntilRequest, LoglikelihoodRequest, diff --git a/src/lighteval/models/adapter_model.py b/src/lighteval/models/transformers/adapter_model.py similarity index 80% rename from src/lighteval/models/adapter_model.py rename to src/lighteval/models/transformers/adapter_model.py index 24de80f40..449c2c1a8 100644 --- a/src/lighteval/models/adapter_model.py +++ b/src/lighteval/models/transformers/adapter_model.py @@ -22,14 +22,14 @@ import logging from contextlib import nullcontext +from dataclasses import dataclass import torch from transformers import AutoModelForCausalLM, PreTrainedTokenizer -from lighteval.models.base_model import BaseModel -from lighteval.models.model_config import AdapterModelConfig +from lighteval.models.transformers.base_model import BaseModel, BaseModelConfig from lighteval.models.utils import _get_dtype -from lighteval.utils.imports import is_peft_available +from lighteval.utils.imports import NO_PEFT_ERROR_MSG, is_peft_available from lighteval.utils.utils import EnvConfig @@ -39,6 +39,24 @@ from peft import PeftModel +@dataclass +class AdapterModelConfig(BaseModelConfig): + # Adapter models have the specificity that they look at the base model (= the parent) for the tokenizer and config + base_model: str = None + + def __post_init__(self): + if not is_peft_available(): + raise ImportError(NO_PEFT_ERROR_MSG) + + if not self.base_model: # must have a default value bc of dataclass inheritance, but can't actually be None + raise ValueError("The base_model argument must not be null for an adapter model config") + + return super().__post_init__() + + def init_configs(self, env_config: EnvConfig): + return self._init_configs(self.base_model, env_config) + + class AdapterModel(BaseModel): def _create_auto_tokenizer(self, config: AdapterModelConfig, env_config: EnvConfig) -> PreTrainedTokenizer: # By default, we look at the model config for the model stored in `base_model` diff --git a/src/lighteval/models/base_model.py b/src/lighteval/models/transformers/base_model.py similarity index 87% rename from src/lighteval/models/base_model.py rename to src/lighteval/models/transformers/base_model.py index fedc56a5c..9b815d2b0 100644 --- a/src/lighteval/models/base_model.py +++ b/src/lighteval/models/transformers/base_model.py @@ -22,6 +22,7 @@ import logging import os +from dataclasses import dataclass from typing import Optional, Tuple, Union import torch @@ -30,12 +31,18 @@ from torch.nn.utils.rnn import pad_sequence from torch.utils.data import DataLoader from tqdm import tqdm -from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig +from transformers import ( + AutoConfig, + AutoModelForCausalLM, + AutoTokenizer, + BitsAndBytesConfig, + GPTQConfig, + PretrainedConfig, +) from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES from lighteval.data import GenerativeTaskDataset, LoglikelihoodDataset, LoglikelihoodSingleTokenDataset from lighteval.models.abstract_model import LightevalModel, ModelInfo -from lighteval.models.model_config import BaseModelConfig from lighteval.models.model_output import ( Batch, GenerativeMultiturnResponse, @@ -43,7 +50,7 @@ LoglikelihoodResponse, LoglikelihoodSingleTokenResponse, ) -from lighteval.models.utils import _get_dtype, _simplify_name, batched +from lighteval.models.utils import _get_dtype, _get_model_sha, _simplify_name, batched from lighteval.tasks.requests import ( GreedyUntilMultiTurnRequest, GreedyUntilRequest, @@ -52,9 +59,15 @@ LoglikelihoodSingleTokenRequest, Request, ) -from lighteval.utils.imports import is_accelerate_available +from lighteval.utils.imports import ( + NO_AUTOGPTQ_ERROR_MSG, + NO_BNB_ERROR_MSG, + is_accelerate_available, + is_autogptq_available, + is_bnb_available, +) from lighteval.utils.parallelism import find_executable_batch_size -from lighteval.utils.utils import EnvConfig, as_list +from lighteval.utils.utils import EnvConfig, as_list, boolstring_to_bool logger = logging.getLogger(__name__) @@ -69,6 +82,145 @@ STARTING_BATCH_SIZE = 512 +@dataclass +class BaseModelConfig: + """ + Base configuration class for models. + + Attributes: + pretrained (str): + HuggingFace Hub model ID name or the path to a pre-trained + model to load. This is effectively the `pretrained_model_name_or_path` + argument of `from_pretrained` in the HuggingFace `transformers` API. + accelerator (Accelerator): accelerator to use for model training. + tokenizer (Optional[str]): HuggingFace Hub tokenizer ID that will be + used for tokenization. + multichoice_continuations_start_space (Optional[bool]): Whether to add a + space at the start of each continuation in multichoice generation. + For example, context: "What is the capital of France?" and choices: "Paris", "London". + Will be tokenized as: "What is the capital of France? Paris" and "What is the capital of France? London". + True adds a space, False strips a space, None does nothing + pairwise_tokenization (bool): Whether to tokenize the context and continuation as separately or together. + subfolder (Optional[str]): The subfolder within the model repository. + revision (str): The revision of the model. + batch_size (int): The batch size for model training. + max_gen_toks (Optional[int]): The maximum number of tokens to generate. + max_length (Optional[int]): The maximum length of the generated output. + add_special_tokens (bool, optional, defaults to True): Whether to add special tokens to the input sequences. + If `None`, the default value will be set to `True` for seq2seq models (e.g. T5) and + `False` for causal models. + model_parallel (bool, optional, defaults to False): + True/False: force to use or not the `accelerate` library to load a large + model across multiple devices. + Default: None which corresponds to comparing the number of processes with + the number of GPUs. If it's smaller => model-parallelism, else not. + dtype (Union[str, torch.dtype], optional, defaults to None):): + Converts the model weights to `dtype`, if specified. Strings get + converted to `torch.dtype` objects (e.g. `float16` -> `torch.float16`). + Use `dtype="auto"` to derive the type from the model's weights. + device (Union[int, str]): device to use for model training. + quantization_config (Optional[BitsAndBytesConfig]): quantization + configuration for the model, manually provided to load a normally floating point + model at a quantized precision. Needed for 4-bit and 8-bit precision. + trust_remote_code (bool): Whether to trust remote code during model + loading. + + Methods: + __post_init__(): Performs post-initialization checks on the configuration. + _init_configs(model_name, env_config): Initializes the model configuration. + init_configs(env_config): Initializes the model configuration using the environment configuration. + get_model_sha(): Retrieves the SHA of the model. + + """ + + pretrained: str + accelerator: "Accelerator" = None + tokenizer: Optional[str] = None + multichoice_continuations_start_space: Optional[bool] = None + pairwise_tokenization: bool = False + subfolder: Optional[str] = None + revision: str = "main" + batch_size: int = -1 + max_gen_toks: Optional[int] = 256 + max_length: Optional[int] = None + add_special_tokens: bool = True + model_parallel: Optional[bool] = None + dtype: Optional[Union[str, torch.dtype]] = None + device: Union[int, str] = "cuda" + quantization_config: Optional[BitsAndBytesConfig] = None + trust_remote_code: bool = False + use_chat_template: bool = False + compile: bool = False + + def __post_init__(self): + # Making sure this parameter is a boolean + self.multichoice_continuations_start_space = boolstring_to_bool(self.multichoice_continuations_start_space) + + if self.multichoice_continuations_start_space is not None: + if self.multichoice_continuations_start_space: + logger.info( + "You set `multichoice_continuations_start_space` to true. This will force multichoice continuations to use a starting space" + ) + else: + logger.info( + "You set `multichoice_continuations_start_space` to false. This will remove a leading space from multichoice continuations, if present." + ) + + self.model_parallel = boolstring_to_bool(self.model_parallel) + self.compile = boolstring_to_bool(self.compile) + + if self.quantization_config is not None and not is_bnb_available(): + raise ImportError(NO_BNB_ERROR_MSG) + + if not isinstance(self.pretrained, str): + raise ValueError("Pretrained model name must be passed as string.") + if not isinstance(self.device, str): + raise ValueError("Current device must be passed as string.") + + def _init_configs(self, model_name: str, env_config: EnvConfig) -> PretrainedConfig: + revision = self.revision + if self.subfolder: + revision = f"{self.revision}/{self.subfolder}" + auto_config = AutoConfig.from_pretrained( + model_name, + revision=revision, + trust_remote_code=self.trust_remote_code, + cache_dir=env_config.cache_dir, + token=env_config.token, + ) + + # Gathering the model's automatic quantization config, if available + try: + model_auto_quantization_config = auto_config.quantization_config + logger.info("An automatic quantization config was found in the model's config. Using it to load the model") + except (AttributeError, KeyError): + model_auto_quantization_config = None + + if model_auto_quantization_config is not None: + if self.quantization_config is not None: + # We don't load models quantized by default with a different user provided conf + raise ValueError("You manually requested quantization on a model already quantized!") + + # We add the quantization to the model params we store + if model_auto_quantization_config["quant_method"] == "gptq": + if not is_autogptq_available(): + raise ImportError(NO_AUTOGPTQ_ERROR_MSG) + auto_config.quantization_config["use_exllama"] = None + self.quantization_config = GPTQConfig(**auto_config.quantization_config, disable_exllama=True) + elif model_auto_quantization_config["quant_method"] == "bitsandbytes": + if not is_bnb_available(): + raise ImportError(NO_BNB_ERROR_MSG) + self.quantization_config = BitsAndBytesConfig(**auto_config.quantization_config) + + return auto_config + + def init_configs(self, env_config: EnvConfig) -> PretrainedConfig: + return self._init_configs(self.pretrained, env_config=env_config) + + def get_model_sha(self): + return _get_model_sha(repo_id=self.pretrained, revision=self.revision) + + class BaseModel(LightevalModel): def __init__( self, diff --git a/src/lighteval/models/delta_model.py b/src/lighteval/models/transformers/delta_model.py similarity index 81% rename from src/lighteval/models/delta_model.py rename to src/lighteval/models/transformers/delta_model.py index 9aa8c01d5..20780f1e7 100644 --- a/src/lighteval/models/delta_model.py +++ b/src/lighteval/models/transformers/delta_model.py @@ -22,20 +22,37 @@ import logging from contextlib import nullcontext +from dataclasses import dataclass import torch from tqdm import tqdm from transformers import AutoModelForCausalLM -from lighteval.models.base_model import BaseModel -from lighteval.models.model_config import DeltaModelConfig -from lighteval.models.utils import _get_dtype +from lighteval.models.transformers.base_model import BaseModel, BaseModelConfig +from lighteval.models.utils import _get_dtype, _get_model_sha from lighteval.utils.utils import EnvConfig logger = logging.getLogger(__name__) +@dataclass +class DeltaModelConfig(BaseModelConfig): + # Delta models look at the pretrained (= the delta weights) for the tokenizer and model config + base_model: str = None + + def __post_init__(self): + self.revision = "main" + + if not self.base_model: # must have a default value bc of dataclass inheritance, but can't actually be None + raise ValueError("The base_model argument must not be null for a delta model config") + + return super().__post_init__() + + def get_model_sha(self): + return _get_model_sha(repo_id=self.pretrained, revision="main") + + class DeltaModel(BaseModel): def _create_auto_model( self, diff --git a/src/lighteval/models/vllm_model.py b/src/lighteval/models/vllm/vllm_model.py similarity index 92% rename from src/lighteval/models/vllm_model.py rename to src/lighteval/models/vllm/vllm_model.py index ecfe8fd8b..2d413807d 100644 --- a/src/lighteval/models/vllm_model.py +++ b/src/lighteval/models/vllm/vllm_model.py @@ -24,6 +24,7 @@ import itertools import logging import os +from dataclasses import dataclass from typing import Optional import torch @@ -31,7 +32,6 @@ from lighteval.data import GenerativeTaskDataset, LoglikelihoodDataset from lighteval.models.abstract_model import LightevalModel, ModelInfo -from lighteval.models.model_config import VLLMModelConfig from lighteval.models.model_output import ( GenerativeResponse, LoglikelihoodResponse, @@ -66,6 +66,30 @@ STARTING_BATCH_SIZE = 512 +@dataclass +class VLLMModelConfig: + pretrained: str + gpu_memory_utilisation: float = 0.9 # lower this if you are running out of memory + revision: str = "main" # revision of the model + dtype: str | None = None + tensor_parallel_size: int = 1 # how many GPUs to use for tensor parallelism + pipeline_parallel_size: int = 1 # how many GPUs to use for pipeline parallelism + data_parallel_size: int = 1 # how many GPUs to use for data parallelism + max_model_length: int | None = None # maximum length of the model, ussually infered automatically. reduce this if you encouter OOM issues, 4096 is usually enough + swap_space: int = 4 # CPU swap space size (GiB) per GPU. + seed: int = 1234 + trust_remote_code: bool = False + use_chat_template: bool = False + add_special_tokens: bool = True + multichoice_continuations_start_space: bool = ( + True # whether to add a space at the start of each continuation in multichoice generation + ) + pairwise_tokenization: bool = False # whether to tokenize the context and continuation separately or together. + + subfolder: Optional[str] = None + temperature: float = 0.6 # will be used for multi sampling tasks, for tasks requiring no sampling, this will be ignored and set to 0. + + class VLLMModel(LightevalModel): def __init__( self, diff --git a/src/lighteval/tasks/lighteval_task.py b/src/lighteval/tasks/lighteval_task.py index 9d08ba124..58724242e 100644 --- a/src/lighteval/tasks/lighteval_task.py +++ b/src/lighteval/tasks/lighteval_task.py @@ -41,7 +41,7 @@ apply_target_perplexity_metric, ) from lighteval.metrics.metrics import Metric, MetricCategory, Metrics -from lighteval.models.base_model import BaseModel +from lighteval.models.transformers.base_model import BaseModel from lighteval.tasks.prompt_manager import PromptManager from lighteval.tasks.requests import ( Doc, diff --git a/tests/models/test_abstract_model.py b/tests/models/test_abstract_model.py index a598bdc42..e7fc0172e 100644 --- a/tests/models/test_abstract_model.py +++ b/tests/models/test_abstract_model.py @@ -22,8 +22,7 @@ from transformers import AutoTokenizer -from lighteval.models.dummy_model import DummyModel -from lighteval.models.model_config import DummyModelConfig +from lighteval.models.dummy.dummy_model import DummyModel, DummyModelConfig from lighteval.utils.utils import EnvConfig diff --git a/tests/models/test_base_model.py b/tests/models/test_base_model.py index dac396f5d..4f26d2924 100644 --- a/tests/models/test_base_model.py +++ b/tests/models/test_base_model.py @@ -20,9 +20,8 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -from lighteval.models.base_model import BaseModel -from lighteval.models.model_config import BaseModelConfig from lighteval.models.model_loader import load_model +from lighteval.models.transformers.base_model import BaseModel, BaseModelConfig from lighteval.utils.utils import EnvConfig From c0966cfd78629b1d1387fe4c94dfd226535ad75b Mon Sep 17 00:00:00 2001 From: Albert Villanova del Moral <8515462+albertvillanova@users.noreply.github.com> Date: Tue, 10 Dec 2024 15:54:07 +0100 Subject: [PATCH 03/12] Add trufflehog secrets detection (#429) --- .github/workflows/trufflehog.yml | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) create mode 100644 .github/workflows/trufflehog.yml diff --git a/.github/workflows/trufflehog.yml b/.github/workflows/trufflehog.yml new file mode 100644 index 000000000..8ac08ad65 --- /dev/null +++ b/.github/workflows/trufflehog.yml @@ -0,0 +1,19 @@ +on: + push: + +name: Scan Secret Leaks + +permissions: + contents: read + +jobs: + trufflehog: + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v4 + with: + fetch-depth: 0 + - name: Secret Scanning + uses: trufflesecurity/trufflehog@main + From 075a26603e40c8ba31ce21e0019c6714cf32413b Mon Sep 17 00:00:00 2001 From: Albert Villanova del Moral <8515462+albertvillanova@users.noreply.github.com> Date: Tue, 10 Dec 2024 17:11:58 +0100 Subject: [PATCH 04/12] Update docs about inference endpoints (#432) * Delete type and rename model in endpoint docs * Explain to pass either model_name or endpoint_name+reuse_existing * Fix legacy instance type and size in docs * Minor fix --- ...valuate-the-model-on-a-server-or-container.mdx | 15 +++++++-------- examples/model_configs/endpoint_model.yaml | 8 +++++--- src/lighteval/models/endpoints/endpoint_model.py | 2 +- 3 files changed, 13 insertions(+), 12 deletions(-) diff --git a/docs/source/evaluate-the-model-on-a-server-or-container.mdx b/docs/source/evaluate-the-model-on-a-server-or-container.mdx index 0d9a7d127..fff5f777c 100644 --- a/docs/source/evaluate-the-model-on-a-server-or-container.mdx +++ b/docs/source/evaluate-the-model-on-a-server-or-container.mdx @@ -26,22 +26,22 @@ __configuration file example:__ ```yaml model: - type: "endpoint" base_params: - endpoint_name: "llama-2-7B-lighteval" # needs to be lower case without special characters - model: "meta-llama/Llama-2-7b-hf" + # Pass either model_name, or endpoint_name and true reuse_existing + # endpoint_name: "llama-2-7B-lighteval" # needs to be lower case without special characters + # reuse_existing: true # defaults to false; if true, ignore all params in instance, and don't delete the endpoint after evaluation + model_name: "meta-llama/Llama-2-7b-hf" revision: "main" dtype: "float16" # can be any of "awq", "eetq", "gptq", "4bit' or "8bit" (will use bitsandbytes), "bfloat16" or "float16" - reuse_existing: false # if true, ignore all params in instance, and don't delete the endpoint after evaluation instance: accelerator: "gpu" region: "eu-west-1" vendor: "aws" - instance_size: "medium" - instance_type: "g5.2xlarge" + instance_type: "nvidia-a10g" + instance_size: "x1" framework: "pytorch" endpoint_type: "protected" - namespace: null # The namespace under which to launch the endopint. Defaults to the current user's namespace + namespace: null # The namespace under which to launch the endpoint. Defaults to the current user's namespace image_url: null # Optionally specify the docker image to use when launching the endpoint model. E.g., launching models with later releases of the TGI container with support for newer models. env_vars: null # Optional environment variables to include when launching the endpoint. e.g., `MAX_INPUT_LENGTH: 2048` @@ -58,7 +58,6 @@ __configuration file example:__ ```yaml model: - type: "tgi" instance: inference_server_address: "" inference_server_auth: null diff --git a/examples/model_configs/endpoint_model.yaml b/examples/model_configs/endpoint_model.yaml index 3cca5c431..79b7eff0b 100644 --- a/examples/model_configs/endpoint_model.yaml +++ b/examples/model_configs/endpoint_model.yaml @@ -1,15 +1,17 @@ model: base_params: - model_name: "meta-llama/Llama-2-7b-hf" # the model name or the endpoint name if reuse_existing is true + # Pass either model_name, or endpoint_name and true reuse_existing + # endpoint_name: "llama-2-7B-lighteval" # needs to be lower case without special characters + # reuse_existing: true # defaults to false; if true, ignore all params in instance, and don't delete the endpoint after evaluation + model_name: "meta-llama/Llama-2-7b-hf" revision: "main" dtype: "float16" # can be any of "awq", "eetq", "gptq", "4bit' or "8bit" (will use bitsandbytes), "bfloat16" or "float16" - reuse_existing: false # if true, ignore all params in instance, and don't delete the endpoint after evaluation instance: accelerator: "gpu" region: "eu-west-1" vendor: "aws" - instance_size: "x1" instance_type: "nvidia-a10g" + instance_size: "x1" framework: "pytorch" endpoint_type: "protected" namespace: null # The namespace under which to launch the endopint. Defaults to the current user's namespace diff --git a/src/lighteval/models/endpoints/endpoint_model.py b/src/lighteval/models/endpoints/endpoint_model.py index 112338964..3b08940fb 100644 --- a/src/lighteval/models/endpoints/endpoint_model.py +++ b/src/lighteval/models/endpoints/endpoint_model.py @@ -95,7 +95,7 @@ class InferenceEndpointModelConfig: endpoint_type: str = "protected" add_special_tokens: bool = True revision: str = "main" - namespace: str = None # The namespace under which to launch the endopint. Defaults to the current user's namespace + namespace: str = None # The namespace under which to launch the endpoint. Defaults to the current user's namespace image_url: str = None env_vars: dict = None From f2e1f6970289f33aa42337bcbb51b00f71deee57 Mon Sep 17 00:00:00 2001 From: Albert Villanova del Moral <8515462+albertvillanova@users.noreply.github.com> Date: Tue, 10 Dec 2024 17:55:20 +0100 Subject: [PATCH 05/12] Fix ignored reuse_existing in config file (#431) * Align should_reuse_existing with reuse_existing * Align reuse_existing default to False --- src/lighteval/main_endpoint.py | 2 +- src/lighteval/models/endpoints/endpoint_model.py | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/src/lighteval/main_endpoint.py b/src/lighteval/main_endpoint.py index d17da4325..4b31f0f2d 100644 --- a/src/lighteval/main_endpoint.py +++ b/src/lighteval/main_endpoint.py @@ -231,7 +231,7 @@ def inference_endpoint( "endpoint_name": config["base_params"].get("endpoint_name", None), "model_dtype": config["base_params"].get("dtype", None), "revision": config["base_params"].get("revision", None) or "main", - "should_reuse_existing": config["base_params"].get("should_reuse_existing"), + "reuse_existing": config["base_params"].get("reuse_existing"), "accelerator": config.get("instance", {}).get("accelerator", None), "region": config.get("instance", {}).get("region", None), "vendor": config.get("instance", {}).get("vendor", None), diff --git a/src/lighteval/models/endpoints/endpoint_model.py b/src/lighteval/models/endpoints/endpoint_model.py index 3b08940fb..909d4795d 100644 --- a/src/lighteval/models/endpoints/endpoint_model.py +++ b/src/lighteval/models/endpoints/endpoint_model.py @@ -84,7 +84,7 @@ class InferenceModelConfig: class InferenceEndpointModelConfig: endpoint_name: str = None model_name: str = None - should_reuse_existing: bool = False + reuse_existing: bool = False accelerator: str = "gpu" model_dtype: str = None # if empty, we use the default vendor: str = "aws" @@ -135,7 +135,7 @@ class InferenceEndpointModel(LightevalModel): def __init__( # noqa: C901 self, config: Union[InferenceEndpointModelConfig, InferenceModelConfig], env_config: EnvConfig ) -> None: - self.reuse_existing = getattr(config, "should_reuse_existing", True) + self.reuse_existing = getattr(config, "reuse_existing", False) self._max_length = None self.endpoint = None self.model_name = None @@ -171,7 +171,7 @@ def __init__( # noqa: C901 ): try: if self.endpoint is None: # Endpoint does not exist yet locally - if not config.should_reuse_existing: # New endpoint + if not config.reuse_existing: # New endpoint logger.info("Creating endpoint.") self.endpoint: InferenceEndpoint = create_inference_endpoint( name=endpoint_name, @@ -239,7 +239,7 @@ def __init__( # noqa: C901 # The endpoint actually already exists, we'll spin it up instead of trying to create a new one if "409 Client Error: Conflict for url:" in str(e): config.endpoint_name = endpoint_name - config.should_reuse_existing = True + config.reuse_existing = True # Requested resources are not available elif "Bad Request: Compute instance not available yet" in str(e): logger.error( From 6ad7276e07efb3b383e6da42019f3e729c45a8e8 Mon Sep 17 00:00:00 2001 From: Parag Ekbote Date: Wed, 11 Dec 2024 15:44:13 +0530 Subject: [PATCH 06/12] Deprecate Obsolete Config Properties (#433) * Deprecate obsolete config properties. --- src/lighteval/metrics/__init__.py | 7 +- src/lighteval/tasks/default_tasks.py | 2463 ------------------------- src/lighteval/tasks/lighteval_task.py | 6 - 3 files changed, 1 insertion(+), 2475 deletions(-) diff --git a/src/lighteval/metrics/__init__.py b/src/lighteval/metrics/__init__.py index 8ca514f96..f3d51941a 100644 --- a/src/lighteval/metrics/__init__.py +++ b/src/lighteval/metrics/__init__.py @@ -20,7 +20,6 @@ # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. -import re from lighteval.metrics.metrics import Metric, MetricCategory from lighteval.models.model_output import ModelResponse @@ -89,7 +88,6 @@ def apply_generative_metric( # noqa: C901 responses: list[list[ModelResponse]], formatted_docs: list[Doc], metrics: list[Metric], - output_regex: str = None, ): outputs = [] @@ -113,10 +111,7 @@ def apply_generative_metric( # noqa: C901 preds = [] for pred_raw in preds_raw: - if output_regex is not None: - pred = next(iter(re.findall(output_regex, pred_raw)), "") - else: - pred = pred_raw + pred = pred_raw preds.append(pred) for metric in metrics: diff --git a/src/lighteval/tasks/default_tasks.py b/src/lighteval/tasks/default_tasks.py index fa5ce3707..59254a971 100644 --- a/src/lighteval/tasks/default_tasks.py +++ b/src/lighteval/tasks/default_tasks.py @@ -37,8 +37,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -55,8 +53,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -73,8 +69,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -91,8 +85,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -109,8 +101,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -127,8 +117,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -145,8 +133,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -163,8 +149,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -181,8 +165,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -199,8 +181,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -217,8 +197,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -235,8 +213,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -253,8 +229,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -271,8 +245,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -289,8 +261,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -307,8 +277,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -325,8 +293,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -343,8 +309,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -361,8 +325,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -379,8 +341,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -397,8 +357,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -415,8 +373,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -433,8 +389,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -451,8 +405,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -469,8 +421,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -487,8 +437,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -505,8 +453,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -523,8 +469,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -541,8 +485,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -559,8 +501,6 @@ generation_size=-1, metric=[Metrics.acc_golds_likelihood], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -577,8 +517,6 @@ generation_size=-1, metric=[Metrics.acc_golds_likelihood], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -595,8 +533,6 @@ generation_size=-1, metric=[Metrics.acc_golds_likelihood], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -613,8 +549,6 @@ generation_size=-1, metric=[Metrics.acc_golds_likelihood], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -631,8 +565,6 @@ generation_size=-1, metric=[Metrics.acc_golds_likelihood], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -649,8 +581,6 @@ generation_size=-1, metric=[Metrics.acc_golds_likelihood], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -667,8 +597,6 @@ generation_size=-1, metric=[Metrics.acc_golds_likelihood], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -685,8 +613,6 @@ generation_size=-1, metric=[Metrics.acc_golds_likelihood], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -703,8 +629,6 @@ generation_size=-1, metric=[Metrics.acc_golds_likelihood], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -721,8 +645,6 @@ generation_size=-1, metric=[Metrics.acc_golds_likelihood], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -739,8 +661,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -757,8 +677,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -775,8 +693,6 @@ generation_size=-1, metric=[Metrics.acc_golds_likelihood], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -793,8 +709,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -811,8 +725,6 @@ generation_size=1, metric=[Metrics.bleu], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -829,7 +741,6 @@ generation_size=100, metric=[Metrics.perfect_exact_match], stop_sequence=None, - output_regex="[^\\.\\?\\!\\;\\n]+", trust_dataset=True, version=0, ) @@ -851,8 +762,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -869,8 +778,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -887,8 +794,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -905,8 +810,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -923,8 +826,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -941,8 +842,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -959,8 +858,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -977,8 +874,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -995,8 +890,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1013,8 +906,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1031,8 +922,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1049,8 +938,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1067,8 +954,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1085,8 +970,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1103,8 +986,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1121,8 +1002,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1139,8 +1018,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1157,8 +1034,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1175,8 +1050,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1193,8 +1066,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, must_remove_duplicate_docs=True, trust_dataset=True, version=0, @@ -1212,8 +1083,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, must_remove_duplicate_docs=True, trust_dataset=True, version=0, @@ -1231,8 +1100,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, must_remove_duplicate_docs=True, trust_dataset=True, version=0, @@ -1250,8 +1117,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, must_remove_duplicate_docs=True, trust_dataset=True, version=0, @@ -1269,8 +1134,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, must_remove_duplicate_docs=True, trust_dataset=True, version=0, @@ -1288,8 +1151,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, must_remove_duplicate_docs=True, trust_dataset=True, version=0, @@ -1307,8 +1168,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, must_remove_duplicate_docs=True, trust_dataset=True, version=0, @@ -1326,8 +1185,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, must_remove_duplicate_docs=True, trust_dataset=True, version=0, @@ -1345,8 +1202,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, must_remove_duplicate_docs=True, trust_dataset=True, version=0, @@ -1364,8 +1219,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, must_remove_duplicate_docs=True, trust_dataset=True, version=0, @@ -1383,8 +1236,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, must_remove_duplicate_docs=True, trust_dataset=True, version=0, @@ -1402,8 +1253,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, must_remove_duplicate_docs=True, trust_dataset=True, version=0, @@ -1421,8 +1270,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, must_remove_duplicate_docs=True, trust_dataset=True, version=0, @@ -1440,8 +1287,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, must_remove_duplicate_docs=True, trust_dataset=True, version=0, @@ -1459,8 +1304,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, must_remove_duplicate_docs=True, trust_dataset=True, version=0, @@ -1478,8 +1321,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, must_remove_duplicate_docs=True, trust_dataset=True, version=0, @@ -1497,8 +1338,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, must_remove_duplicate_docs=True, trust_dataset=True, version=0, @@ -1516,8 +1355,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, must_remove_duplicate_docs=True, trust_dataset=True, version=0, @@ -1541,8 +1378,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1565,8 +1400,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1589,8 +1422,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1613,8 +1444,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1637,8 +1466,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1661,8 +1488,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1685,8 +1510,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1709,8 +1532,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1733,8 +1554,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1757,8 +1576,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1781,8 +1598,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1805,8 +1620,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1829,8 +1642,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1853,8 +1664,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1877,8 +1686,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1901,8 +1708,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1925,8 +1730,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1949,8 +1752,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1973,8 +1774,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -1997,8 +1796,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2021,8 +1818,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2045,8 +1840,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2069,8 +1862,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2093,8 +1884,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2117,8 +1906,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2141,8 +1928,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2165,8 +1950,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["", "Q=", "\n\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2189,8 +1972,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2213,8 +1994,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2237,8 +2016,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2261,8 +2038,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2285,8 +2060,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2309,8 +2082,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2333,8 +2104,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2357,8 +2126,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2381,8 +2148,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2405,8 +2170,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2429,8 +2192,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2453,8 +2214,6 @@ Metrics.perfect_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2471,8 +2230,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2489,8 +2246,6 @@ generation_size=100, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2512,8 +2267,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2535,8 +2288,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2558,8 +2309,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2581,8 +2330,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2604,8 +2351,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2627,8 +2372,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2650,8 +2393,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2673,8 +2414,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2696,8 +2435,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2719,8 +2456,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2742,8 +2477,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2765,8 +2498,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2788,8 +2519,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2811,8 +2540,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2834,8 +2561,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2857,8 +2582,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2880,8 +2603,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2903,8 +2624,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2926,8 +2645,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2949,8 +2666,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2972,8 +2687,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -2995,8 +2708,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3018,8 +2729,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3041,8 +2750,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3059,8 +2766,6 @@ generation_size=100, metric=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3077,8 +2782,6 @@ generation_size=100, metric=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3095,8 +2798,6 @@ generation_size=100, metric=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3113,8 +2814,6 @@ generation_size=100, metric=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3131,8 +2830,6 @@ generation_size=100, metric=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3149,8 +2846,6 @@ generation_size=100, metric=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3167,8 +2862,6 @@ generation_size=100, metric=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3185,8 +2878,6 @@ generation_size=100, metric=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3203,8 +2894,6 @@ generation_size=100, metric=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3221,8 +2910,6 @@ generation_size=100, metric=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3239,8 +2926,6 @@ generation_size=100, metric=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3257,8 +2942,6 @@ generation_size=100, metric=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3275,8 +2958,6 @@ generation_size=100, metric=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3293,8 +2974,6 @@ generation_size=100, metric=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3311,8 +2990,6 @@ generation_size=100, metric=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3329,8 +3006,6 @@ generation_size=100, metric=[Metrics.rouge1, Metrics.rouge2, Metrics.rougeL], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3352,8 +3027,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3375,8 +3048,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3398,8 +3069,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3421,8 +3090,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3444,8 +3111,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3462,8 +3127,6 @@ generation_size=100, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3485,8 +3148,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3508,8 +3169,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3531,8 +3190,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3554,8 +3211,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3577,8 +3232,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3600,8 +3253,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3618,8 +3269,6 @@ generation_size=100, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3636,8 +3285,6 @@ generation_size=100, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3659,8 +3306,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3677,8 +3322,6 @@ generation_size=100, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3700,8 +3343,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3723,8 +3364,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3746,8 +3385,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3769,8 +3406,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3792,8 +3427,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3815,8 +3448,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3838,8 +3469,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3861,8 +3490,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3884,8 +3511,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3907,8 +3532,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3925,8 +3548,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3943,8 +3564,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3961,8 +3580,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3979,8 +3596,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -3997,8 +3612,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4015,8 +3628,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4033,8 +3644,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4051,8 +3660,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4069,8 +3676,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4087,8 +3692,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4105,8 +3708,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4123,8 +3724,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4141,8 +3740,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4159,8 +3756,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4177,8 +3772,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4195,8 +3788,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4213,8 +3804,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4231,8 +3820,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4249,8 +3836,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4267,8 +3852,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4285,8 +3868,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4303,8 +3884,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4321,8 +3900,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4339,8 +3916,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4357,8 +3932,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4375,8 +3948,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4393,8 +3964,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4411,8 +3980,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4429,8 +3996,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4447,8 +4012,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4465,8 +4028,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4483,8 +4044,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4501,8 +4060,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4519,8 +4076,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4537,8 +4092,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4555,8 +4108,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4573,8 +4124,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4591,8 +4140,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4609,8 +4156,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4627,8 +4172,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4645,8 +4188,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4663,8 +4204,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4681,8 +4220,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4699,8 +4236,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4717,8 +4252,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4735,8 +4268,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4753,8 +4284,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4771,8 +4300,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4789,8 +4316,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4807,8 +4332,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4825,8 +4348,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4843,8 +4364,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4861,8 +4380,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4879,8 +4396,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4897,8 +4412,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4915,8 +4428,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4933,8 +4444,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4951,8 +4460,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4969,8 +4476,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -4987,8 +4492,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5005,8 +4508,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5023,8 +4524,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5041,8 +4540,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5059,8 +4556,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5077,8 +4572,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5095,8 +4588,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5113,8 +4604,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5131,8 +4620,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5149,8 +4636,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5167,8 +4652,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5185,8 +4668,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5203,8 +4684,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5221,8 +4700,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5239,8 +4716,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5257,8 +4732,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5275,8 +4748,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5293,8 +4764,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5311,8 +4780,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5329,8 +4796,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5347,8 +4812,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5365,8 +4828,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5383,8 +4844,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5401,8 +4860,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5419,8 +4876,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5437,8 +4892,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5455,8 +4908,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5473,8 +4924,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5491,8 +4940,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5509,8 +4956,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5527,8 +4972,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5545,8 +4988,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5563,8 +5004,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5581,8 +5020,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5599,8 +5036,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5617,8 +5052,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5635,8 +5068,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5653,8 +5084,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5671,8 +5100,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5689,8 +5116,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5707,8 +5132,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5725,8 +5148,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5743,8 +5164,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5761,8 +5180,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5779,8 +5196,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5797,8 +5212,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5815,8 +5228,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5833,8 +5244,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5851,8 +5260,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5869,8 +5276,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5887,8 +5292,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5905,8 +5308,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5923,8 +5324,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5941,8 +5340,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5959,8 +5356,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5977,8 +5372,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -5995,8 +5388,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6013,8 +5404,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6031,8 +5420,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6049,8 +5436,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6067,8 +5452,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6085,8 +5468,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6103,8 +5484,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6121,8 +5500,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6139,8 +5516,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6157,8 +5532,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6175,8 +5548,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6193,8 +5564,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6211,8 +5580,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6229,8 +5596,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6247,8 +5612,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6265,8 +5628,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6283,8 +5644,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6301,8 +5660,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6319,8 +5676,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6337,8 +5692,6 @@ generation_size=100, metric=[Metrics.prediction_perplexity], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6355,8 +5708,6 @@ generation_size=100, metric=[Metrics.prediction_perplexity], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6373,8 +5724,6 @@ generation_size=100, metric=[Metrics.prediction_perplexity], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6391,8 +5740,6 @@ generation_size=100, metric=[Metrics.prediction_perplexity], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6409,8 +5756,6 @@ generation_size=100, metric=[Metrics.prediction_perplexity], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6427,8 +5772,6 @@ generation_size=100, metric=[Metrics.prediction_perplexity], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6450,8 +5793,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6473,8 +5814,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6491,8 +5830,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6509,8 +5846,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6527,8 +5862,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6545,8 +5878,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6563,8 +5894,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6581,8 +5910,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6599,8 +5926,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6624,8 +5949,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6649,8 +5972,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6674,8 +5995,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6699,8 +6018,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6724,8 +6041,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6749,8 +6064,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6774,8 +6087,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6799,8 +6110,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6824,8 +6133,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6842,8 +6149,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6860,8 +6165,6 @@ generation_size=1, metric=[Metrics.rouge_t5, Metrics.bleu], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6878,8 +6181,6 @@ generation_size=1, metric=[Metrics.rouge_t5, Metrics.bleu, Metrics.loglikelihood_acc, Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6896,8 +6197,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6919,8 +6218,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6937,8 +6234,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6955,7 +6250,6 @@ generation_size=100, metric=[Metrics.rouge_t5, Metrics.bleu, Metrics.perfect_exact_match], stop_sequence=[".", ";", "!", "?"], - output_regex="[^\\.\\?\\!\\;\\n]+", trust_dataset=True, version=0, ) @@ -6972,8 +6266,6 @@ generation_size=1, metric=[Metrics.rouge_t5, Metrics.loglikelihood_acc, Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -6990,8 +6282,6 @@ generation_size=100, metric=[Metrics.copyright], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7008,8 +6298,6 @@ generation_size=100, metric=[Metrics.copyright], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7026,8 +6314,6 @@ generation_size=100, metric=[Metrics.copyright], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7044,8 +6330,6 @@ generation_size=100, metric=[Metrics.copyright], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7062,8 +6346,6 @@ generation_size=100, metric=[Metrics.copyright], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7080,8 +6362,6 @@ generation_size=100, metric=[Metrics.copyright], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7098,8 +6378,6 @@ generation_size=100, metric=[Metrics.copyright], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7116,8 +6394,6 @@ generation_size=100, metric=[Metrics.copyright], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7134,8 +6410,6 @@ generation_size=100, metric=[Metrics.copyright], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7152,8 +6426,6 @@ generation_size=100, metric=[Metrics.copyright], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7170,8 +6442,6 @@ generation_size=100, metric=[Metrics.copyright], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7188,8 +6458,6 @@ generation_size=100, metric=[Metrics.copyright], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7206,8 +6474,6 @@ generation_size=100, metric=[Metrics.copyright], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7224,8 +6490,6 @@ generation_size=100, metric=[Metrics.copyright], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7242,8 +6506,6 @@ generation_size=100, metric=[Metrics.copyright], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7260,8 +6522,6 @@ generation_size=100, metric=[Metrics.copyright], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7278,8 +6538,6 @@ generation_size=100, metric=[Metrics.copyright], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7296,8 +6554,6 @@ generation_size=10, metric=[Metrics.perfect_exact_match, Metrics.f1_score], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7314,8 +6570,6 @@ generation_size=10, metric=[Metrics.perfect_exact_match, Metrics.f1_score], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7332,8 +6586,6 @@ generation_size=128, metric=[Metrics.exact_match, Metrics.quasi_exact_match, Metrics.f1_score, Metrics.rougeL, "bleu_1", "bleu_4"], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7350,8 +6602,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7368,8 +6618,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7386,8 +6634,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7404,8 +6650,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7422,8 +6666,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7440,8 +6682,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7458,8 +6698,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7476,8 +6714,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7494,8 +6730,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7512,8 +6746,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7530,8 +6762,6 @@ generation_size=None, metric=[Metrics.drop], stop_sequence=["."], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7548,8 +6778,6 @@ generation_size=5, metric=[Metrics.exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7566,8 +6794,6 @@ generation_size=5, metric=[Metrics.exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7584,8 +6810,6 @@ generation_size=5, metric=[Metrics.exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7602,8 +6826,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7620,8 +6842,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7638,8 +6858,6 @@ generation_size=1, metric=[Metrics.rouge_t5, Metrics.bleu, Metrics.loglikelihood_acc, Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7656,8 +6874,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7674,8 +6890,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7692,8 +6906,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7710,8 +6922,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7728,8 +6938,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7746,8 +6954,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7769,8 +6975,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7792,8 +6996,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7815,8 +7017,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7838,8 +7038,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7861,8 +7059,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7884,8 +7080,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7907,8 +7101,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7930,8 +7122,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7953,8 +7143,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7976,8 +7164,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -7999,8 +7185,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8022,8 +7206,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8045,8 +7227,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8068,8 +7248,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8091,8 +7269,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8109,8 +7285,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8127,8 +7301,6 @@ generation_size=5, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8145,8 +7317,6 @@ generation_size=5, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8163,8 +7333,6 @@ generation_size=5, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8181,8 +7349,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8199,8 +7365,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8217,8 +7381,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8235,8 +7397,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8253,8 +7413,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8271,8 +7429,6 @@ generation_size=1, metric=[Metrics.bleu, Metrics.bleurt], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8289,8 +7445,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8307,8 +7461,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8325,8 +7477,6 @@ generation_size=1, metric=[Metrics.bleu, Metrics.rouge_t5], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8343,8 +7493,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8361,8 +7509,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8379,8 +7525,6 @@ generation_size=1, metric=[Metrics.rouge_t5, Metrics.bleu, Metrics.loglikelihood_acc, Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8397,8 +7541,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token, Metrics.mcc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8415,8 +7557,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8433,8 +7573,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8451,8 +7589,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, "loglikelihood_f1"], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8469,8 +7605,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8487,8 +7621,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, "loglikelihood_f1"], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8505,8 +7637,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8523,8 +7653,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8541,8 +7669,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8559,8 +7685,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8577,8 +7701,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8595,8 +7717,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8613,8 +7733,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8631,8 +7749,6 @@ generation_size=256, metric=[Metrics.quasi_exact_match_gsm8k], stop_sequence=["Question=", "Question", "="], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8649,8 +7765,6 @@ generation_size=256, metric=[Metrics.quasi_exact_match_gsm8k, Metrics.maj_at_8_gsm8k], stop_sequence=["Question="], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8667,8 +7781,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8685,8 +7797,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8703,8 +7813,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8726,8 +7834,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8744,8 +7850,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8762,8 +7866,6 @@ generation_size=1, metric=[Metrics.bleu, Metrics.rouge_t5, Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8780,8 +7882,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8798,8 +7898,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8816,8 +7914,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8834,8 +7930,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8852,8 +7946,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8870,8 +7962,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8895,8 +7985,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8920,8 +8008,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8938,8 +8024,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8956,8 +8040,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8974,8 +8056,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -8997,8 +8077,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9020,8 +8098,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9043,8 +8119,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9066,8 +8140,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9089,8 +8161,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9112,8 +8182,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9130,8 +8198,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9148,8 +8214,6 @@ generation_size=1, metric=[Metrics.bleu, Metrics.rouge_t5, Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9166,8 +8230,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9184,8 +8246,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9202,8 +8262,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9220,8 +8278,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9238,8 +8294,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9256,8 +8310,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9274,8 +8326,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9292,8 +8342,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9310,8 +8358,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9328,8 +8374,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9346,8 +8390,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9364,8 +8406,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9382,8 +8422,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9400,8 +8438,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9418,8 +8454,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9436,8 +8470,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9454,8 +8486,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9472,8 +8502,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9490,8 +8518,6 @@ generation_size=10, metric=[Metrics.target_perplexity], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9508,8 +8534,6 @@ generation_size=10, metric=[Metrics.target_perplexity], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9526,8 +8550,6 @@ generation_size=10, metric=[Metrics.target_perplexity], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9544,8 +8566,6 @@ generation_size=10, metric=[Metrics.target_perplexity], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9562,8 +8582,6 @@ generation_size=10, metric=[Metrics.target_perplexity], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9580,8 +8598,6 @@ generation_size=10, metric=[Metrics.target_perplexity], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9598,8 +8614,6 @@ generation_size=10, metric=[Metrics.target_perplexity], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9616,8 +8630,6 @@ generation_size=10, metric=[Metrics.target_perplexity], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9634,8 +8646,6 @@ generation_size=10, metric=[Metrics.target_perplexity], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9652,8 +8662,6 @@ generation_size=1, metric=[Metrics.bleu, Metrics.rouge_t5, Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9670,8 +8678,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9695,8 +8701,6 @@ Metrics.bert_score, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9720,8 +8724,6 @@ Metrics.bert_score, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9745,8 +8747,6 @@ Metrics.bert_score, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9769,8 +8769,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9793,8 +8791,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9817,8 +8813,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9841,8 +8835,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9865,8 +8857,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9889,8 +8879,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9913,8 +8901,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9937,8 +8923,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9961,8 +8945,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -9985,8 +8967,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10009,8 +8989,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10033,8 +9011,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10057,8 +9033,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10081,8 +9055,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10099,8 +9071,6 @@ generation_size=20, metric=[Metrics.exact_match, Metrics.quasi_exact_match, Metrics.f1_score], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10123,8 +9093,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10147,8 +9115,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10171,8 +9137,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10195,8 +9159,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10219,8 +9181,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10243,8 +9203,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10267,8 +9225,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10291,8 +9247,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10315,8 +9269,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10339,8 +9291,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10363,8 +9313,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10381,8 +9329,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10399,7 +9345,6 @@ generation_size=100, metric=[Metrics.bleu, Metrics.rouge_t5, Metrics.perfect_exact_match], stop_sequence=None, - output_regex="[^\\.\\?\\!\\;\\n]+", trust_dataset=True, version=0, ) @@ -10416,8 +9361,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10434,8 +9377,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10452,8 +9393,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10470,8 +9409,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10488,8 +9425,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10506,8 +9441,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10529,8 +9462,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10552,8 +9483,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10575,8 +9504,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10598,8 +9525,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10621,8 +9546,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10639,8 +9562,6 @@ generation_size=2048, metric=[Metrics.quasi_exact_match_math, Metrics.maj_at_4_math], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=1, ) @@ -10657,8 +9578,6 @@ generation_size=2048, metric=[Metrics.quasi_exact_match_math, Metrics.maj_at_4_math], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=1, ) @@ -10675,8 +9594,6 @@ generation_size=2048, metric=[Metrics.quasi_exact_match_math, Metrics.maj_at_4_math], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=1, ) @@ -10693,8 +9610,6 @@ generation_size=2048, metric=[Metrics.quasi_exact_match_math, Metrics.maj_at_4_math], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=1, ) @@ -10711,8 +9626,6 @@ generation_size=2048, metric=[Metrics.quasi_exact_match_math, Metrics.maj_at_4_math], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=1, ) @@ -10729,8 +9642,6 @@ generation_size=2048, metric=[Metrics.quasi_exact_match_math, Metrics.maj_at_4_math], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=1, ) @@ -10747,8 +9658,6 @@ generation_size=2048, metric=[Metrics.quasi_exact_match_math, Metrics.maj_at_4_math], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=1, ) @@ -10765,8 +9674,6 @@ generation_size=2048, metric=[Metrics.quasi_exact_match_math, Metrics.maj_at_4_math], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10783,8 +9690,6 @@ generation_size=2048, metric=[Metrics.quasi_exact_match_math, Metrics.maj_at_4_math], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10801,8 +9706,6 @@ generation_size=2048, metric=[Metrics.quasi_exact_match_math, Metrics.maj_at_4_math], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10819,8 +9722,6 @@ generation_size=2048, metric=[Metrics.quasi_exact_match_math, Metrics.maj_at_4_math], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10837,8 +9738,6 @@ generation_size=2048, metric=[Metrics.quasi_exact_match_math, Metrics.maj_at_4_math], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10855,8 +9754,6 @@ generation_size=2048, metric=[Metrics.quasi_exact_match_math, Metrics.maj_at_4_math], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10873,8 +9770,6 @@ generation_size=2048, metric=[Metrics.quasi_exact_match_math, Metrics.maj_at_4_math], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10891,8 +9786,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10909,8 +9802,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10927,8 +9818,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10945,8 +9834,6 @@ generation_size=128, metric=[Metrics.exact_match, Metrics.quasi_exact_match, Metrics.f1_score, Metrics.rougeL, "bleu_1", "bleu_4"], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10963,8 +9850,6 @@ generation_size=128, metric=[Metrics.exact_match, Metrics.quasi_exact_match, Metrics.f1_score, Metrics.rougeL, "bleu_1", "bleu_4"], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -10981,8 +9866,6 @@ generation_size=128, metric=[Metrics.exact_match, Metrics.quasi_exact_match, Metrics.f1_score, Metrics.rougeL, "bleu_1", "bleu_4"], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11005,8 +9888,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11023,8 +9904,6 @@ generation_size=512, metric=[Metrics.exact_match, Metrics.quasi_exact_match, Metrics.f1_score, Metrics.rougeL, "bleu_1", "bleu_4"], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11047,8 +9926,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11065,8 +9942,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11083,8 +9958,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11101,8 +9974,6 @@ generation_size=None, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n", "=", "Question="], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11119,8 +9990,6 @@ generation_size=None, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n", "=", "Pregunta="], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11137,8 +10006,6 @@ generation_size=None, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n", "=", "Question="], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11155,8 +10022,6 @@ generation_size=None, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n", "=", "Frage="], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11173,8 +10038,6 @@ generation_size=None, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n", "=", "\u0417\u0430\u0434\u0430\u0447\u0430="], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11191,8 +10054,6 @@ generation_size=None, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n", "=", "\u95ee\u9898="], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11209,8 +10070,6 @@ generation_size=None, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n", "=", "\u554f\u984c="], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11227,8 +10086,6 @@ generation_size=None, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n", "=", "\u0e42\u0e08\u0e17\u0e22\u0e4c="], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11245,8 +10102,6 @@ generation_size=None, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n", "=", "Swali="], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11263,8 +10118,6 @@ generation_size=None, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n", "=", "\u09aa\u09cd\u09b0\u09b6\u09cd\u09a8="], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11281,8 +10134,6 @@ generation_size=None, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n", "=", "\u0c2a\u0c4d\u0c30\u0c36\u0c4d\u0c28="], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11299,8 +10150,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.rouge_t5], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11317,8 +10166,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11335,8 +10182,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11353,8 +10198,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11371,8 +10214,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11394,8 +10235,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11412,8 +10251,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11430,8 +10267,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11453,8 +10288,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11471,8 +10304,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11489,8 +10320,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11512,8 +10341,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11530,8 +10357,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11548,8 +10373,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11571,8 +10394,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11589,8 +10410,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11607,8 +10426,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11630,8 +10447,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11648,8 +10463,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11666,8 +10479,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11689,8 +10500,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11707,8 +10516,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11725,8 +10532,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11748,8 +10553,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11766,8 +10569,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11784,8 +10585,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11807,8 +10606,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11825,8 +10622,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11843,8 +10638,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11866,8 +10659,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11884,8 +10675,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11902,8 +10691,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11925,8 +10712,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11943,8 +10728,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11961,8 +10744,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -11984,8 +10765,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12002,8 +10781,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12020,8 +10797,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12043,8 +10818,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12061,8 +10834,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12079,8 +10850,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12102,8 +10871,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12120,8 +10887,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12138,8 +10903,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12161,8 +10924,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12179,8 +10940,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12197,8 +10956,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12220,8 +10977,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12238,8 +10993,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12256,8 +11009,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12279,8 +11030,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12297,8 +11046,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12315,8 +11062,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12338,8 +11083,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12356,8 +11099,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12374,8 +11115,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12397,8 +11136,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12415,8 +11152,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12433,8 +11168,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12456,8 +11189,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12474,8 +11205,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12492,8 +11221,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12515,8 +11242,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12533,8 +11258,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12551,8 +11274,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12574,8 +11295,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12592,8 +11311,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12610,8 +11327,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12633,8 +11348,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12651,8 +11364,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12669,8 +11380,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12692,8 +11401,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12710,8 +11417,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12728,8 +11433,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12751,8 +11454,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12769,8 +11470,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12787,8 +11486,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12810,8 +11507,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12828,8 +11523,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12846,8 +11539,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12869,8 +11560,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12887,8 +11576,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12905,8 +11592,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12928,8 +11613,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12946,8 +11629,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12964,8 +11645,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -12987,8 +11666,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13005,8 +11682,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13023,8 +11698,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13046,8 +11719,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13064,8 +11735,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13082,8 +11751,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13105,8 +11772,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13123,8 +11788,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13141,8 +11804,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13164,8 +11825,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13182,8 +11841,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13200,8 +11857,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13223,8 +11878,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13241,8 +11894,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13259,8 +11910,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13282,8 +11931,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13300,8 +11947,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13318,8 +11963,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13341,8 +11984,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13359,8 +12000,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13377,8 +12016,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13400,8 +12037,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13418,8 +12053,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13436,8 +12069,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13459,8 +12090,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13477,8 +12106,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13495,8 +12122,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13518,8 +12143,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13536,8 +12159,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13554,8 +12175,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13577,8 +12196,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13595,8 +12212,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13613,8 +12228,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13636,8 +12249,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13654,8 +12265,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13672,8 +12281,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13695,8 +12302,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13713,8 +12318,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13731,8 +12334,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13754,8 +12355,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13772,8 +12371,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13790,8 +12387,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13813,8 +12408,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13831,8 +12424,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13849,8 +12440,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13872,8 +12461,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13890,8 +12477,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13908,8 +12493,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13931,8 +12514,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13949,8 +12530,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13967,8 +12546,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -13990,8 +12567,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14008,8 +12583,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14026,8 +12599,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14049,8 +12620,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14067,8 +12636,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14085,8 +12652,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14108,8 +12673,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14126,8 +12689,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14144,8 +12705,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14167,8 +12726,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14185,8 +12742,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14203,8 +12758,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14226,8 +12779,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14244,8 +12795,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14262,8 +12811,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14285,8 +12832,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14303,8 +12848,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14321,8 +12864,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14344,8 +12885,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14362,8 +12901,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14380,8 +12917,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14403,8 +12938,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14421,8 +12954,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14439,8 +12970,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14462,8 +12991,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14480,8 +13007,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14498,8 +13023,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14521,8 +13044,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14539,8 +13060,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14557,8 +13076,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14580,8 +13097,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14598,8 +13113,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14616,8 +13129,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14639,8 +13150,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14657,8 +13166,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14675,8 +13182,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14698,8 +13203,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14716,8 +13219,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14734,8 +13235,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14752,8 +13251,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14770,8 +13267,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14788,8 +13283,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14806,8 +13299,6 @@ generation_size=200, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14824,8 +13315,6 @@ generation_size=200, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14842,8 +13331,6 @@ generation_size=200, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14860,8 +13347,6 @@ generation_size=200, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14878,8 +13363,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14896,8 +13379,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14914,8 +13395,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14932,8 +13411,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14950,8 +13427,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14968,8 +13443,6 @@ generation_size=-1, metric=[Metrics.recall_at_1, Metrics.recall_at_2, Metrics.mrr], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -14986,8 +13459,6 @@ generation_size=-1, metric=[Metrics.recall_at_1, Metrics.recall_at_2, Metrics.mrr], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15004,8 +13475,6 @@ generation_size=100, metric=[Metrics.exact_match, Metrics.quasi_exact_match, Metrics.f1_score, Metrics.rougeL, "bleu_1", "bleu_4"], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15022,8 +13491,6 @@ generation_size=1, metric=[Metrics.bleu, Metrics.rouge_t5], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15040,8 +13507,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15058,8 +13523,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15076,8 +13539,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15094,8 +13555,6 @@ generation_size=20, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15112,8 +13571,6 @@ generation_size=20, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15130,8 +13587,6 @@ generation_size=20, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15148,8 +13603,6 @@ generation_size=20, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15166,8 +13619,6 @@ generation_size=20, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15184,8 +13635,6 @@ generation_size=20, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15202,8 +13651,6 @@ generation_size=20, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15220,8 +13667,6 @@ generation_size=20, metric=[Metrics.exact_match, Metrics.quasi_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15238,8 +13683,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15256,8 +13699,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15279,8 +13720,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15297,8 +13736,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15315,7 +13752,6 @@ generation_size=20, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex="([-+]?\\d+)[.]0,1)$", trust_dataset=True, version=0, ) @@ -15332,8 +13768,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15350,8 +13784,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15368,7 +13800,6 @@ generation_size=100, metric=[Metrics.perfect_exact_match], stop_sequence=None, - output_regex="[^\\.\\?\\!\\;\\n]+", trust_dataset=True, version=0, ) @@ -15385,8 +13816,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc, Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15403,8 +13832,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15421,8 +13848,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15439,8 +13864,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15457,8 +13880,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15475,8 +13896,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15493,8 +13912,6 @@ generation_size=1, metric=[Metrics.bleu, Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15511,8 +13928,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15534,8 +13949,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15552,8 +13965,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15570,8 +13981,6 @@ generation_size=1, metric=[Metrics.f1_score], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15588,8 +13997,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15606,8 +14013,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15624,8 +14029,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15647,8 +14050,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15665,8 +14066,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15683,8 +14082,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15701,8 +14098,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15719,8 +14114,6 @@ generation_size=1, metric=[Metrics.bleurt, Metrics.bleu, Metrics.rouge_t5, Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15737,8 +14130,6 @@ generation_size=20, metric=[Metrics.f1_score_quasi], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15755,8 +14146,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15773,8 +14162,6 @@ generation_size=100, metric=[Metrics.exact_match, Metrics.quasi_exact_match, Metrics.f1_score], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15791,8 +14178,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15809,8 +14194,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15834,8 +14217,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15859,8 +14240,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15884,8 +14263,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15909,8 +14286,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15934,8 +14309,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15959,8 +14332,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -15984,8 +14355,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16009,8 +14378,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16034,8 +14401,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16059,8 +14424,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16084,8 +14447,6 @@ Metrics.f1_score_micro, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16102,8 +14463,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16120,8 +14479,6 @@ generation_size=20, metric=[Metrics.prediction_perplexity], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16138,8 +14495,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16156,8 +14511,6 @@ generation_size=100, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16174,8 +14527,6 @@ generation_size=1, metric=[Metrics.rouge_t5, Metrics.bleu, Metrics.loglikelihood_acc, Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16192,8 +14543,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16210,8 +14559,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16228,8 +14575,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16246,8 +14591,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16264,8 +14607,6 @@ generation_size=1, metric=[Metrics.bleu, Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16282,8 +14623,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16300,8 +14639,6 @@ generation_size=1, metric=[Metrics.bleu, Metrics.rouge_t5, Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16318,8 +14655,6 @@ generation_size=1, metric=[Metrics.bleu, Metrics.rouge_t5, Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16336,8 +14671,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16354,8 +14687,6 @@ generation_size=1, metric=[Metrics.bleu, Metrics.rouge_t5, Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16372,8 +14703,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16390,8 +14719,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16408,8 +14735,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16426,8 +14751,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16444,8 +14767,6 @@ generation_size=1, metric=[Metrics.bleu, Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16462,8 +14783,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16480,8 +14799,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16503,8 +14820,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16521,8 +14836,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16539,8 +14852,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16557,8 +14868,6 @@ generation_size=1, metric=[Metrics.f1_score_macro], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16575,8 +14884,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16593,8 +14900,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16611,8 +14916,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16629,8 +14932,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16647,8 +14948,6 @@ generation_size=1, metric=[Metrics.bleu, Metrics.rouge_t5, Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16665,8 +14964,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16683,8 +14980,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16708,8 +15003,6 @@ Metrics.bert_score, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16733,8 +15026,6 @@ Metrics.bert_score, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16758,8 +15049,6 @@ Metrics.bert_score, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16776,8 +15065,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16794,8 +15081,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc_single_token, "multi_f1_numeric"], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16812,8 +15097,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16830,8 +15113,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16848,8 +15129,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16866,8 +15145,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16884,8 +15161,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16902,8 +15177,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16920,8 +15193,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm_nospace], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16938,8 +15209,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16956,8 +15225,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16979,8 +15246,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -16997,8 +15262,6 @@ generation_size=20, metric=[Metrics.exact_match, Metrics.f1_score], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17015,8 +15278,6 @@ generation_size=20, metric=[Metrics.exact_match, Metrics.f1_score], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17038,8 +15299,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17061,8 +15320,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17079,8 +15336,6 @@ generation_size=1, metric=[Metrics.bleu, Metrics.rouge_t5], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17097,8 +15352,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17115,8 +15368,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17133,8 +15384,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17151,8 +15400,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17169,8 +15416,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17187,8 +15432,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17205,8 +15448,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17223,8 +15464,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17241,8 +15480,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17259,8 +15496,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17277,8 +15512,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17295,8 +15528,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17313,8 +15544,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17331,8 +15560,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17349,8 +15576,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17367,8 +15592,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17385,8 +15608,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17403,8 +15624,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17421,8 +15640,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17439,8 +15656,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17457,8 +15672,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17475,8 +15688,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17493,8 +15704,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17511,8 +15720,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17529,8 +15736,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17547,8 +15752,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17565,8 +15768,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17583,8 +15784,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17601,8 +15800,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17619,8 +15816,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17637,8 +15832,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17655,8 +15848,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17673,8 +15864,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17691,8 +15880,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17709,8 +15896,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17727,8 +15912,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17745,8 +15928,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17763,8 +15944,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17781,8 +15960,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17799,8 +15976,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17817,8 +15992,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17835,8 +16008,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17853,8 +16024,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17871,8 +16040,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17889,8 +16056,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc, Metrics.loglikelihood_acc_norm], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17907,8 +16072,6 @@ generation_size=1, metric=[Metrics.bleu, Metrics.rouge_t5, Metrics.loglikelihood_acc, Metrics.bleurt], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17925,8 +16088,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17943,8 +16104,6 @@ generation_size=20, metric=[Metrics.quasi_exact_match_triviaqa], stop_sequence=["\n", ".", ","], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17961,8 +16120,6 @@ generation_size=200, metric=[Metrics.bleu, Metrics.rouge_t5], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -17979,8 +16136,6 @@ generation_size=-1, metric=[Metrics.truthfulqa_mc_metrics], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18003,8 +16158,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18021,8 +16174,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18039,8 +16190,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18057,8 +16206,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18075,8 +16222,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18093,8 +16238,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18111,8 +16254,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18129,8 +16270,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18147,8 +16286,6 @@ generation_size=5, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18165,8 +16302,6 @@ generation_size=5, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18183,8 +16318,6 @@ generation_size=5, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18201,8 +16334,6 @@ generation_size=5, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18219,8 +16350,6 @@ generation_size=5, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18237,8 +16366,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18255,8 +16382,6 @@ generation_size=-1, metric=[Metrics.acc_golds_likelihood], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18273,8 +16398,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18291,8 +16414,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18314,8 +16435,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18337,8 +16456,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18360,8 +16477,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18383,8 +16498,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18406,8 +16519,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18429,8 +16540,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18452,8 +16561,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18475,8 +16582,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18498,8 +16603,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18521,8 +16624,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18544,8 +16645,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18567,8 +16666,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18590,8 +16687,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18613,8 +16708,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18636,8 +16729,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18659,8 +16750,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18682,8 +16771,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18705,8 +16792,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18728,8 +16813,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18751,8 +16834,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18774,8 +16855,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18797,8 +16876,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18820,8 +16897,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18843,8 +16918,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18866,8 +16939,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18889,8 +16960,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18912,8 +16981,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18935,8 +17002,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18958,8 +17023,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -18981,8 +17044,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19004,8 +17065,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19027,8 +17086,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19050,8 +17107,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19073,8 +17128,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19096,8 +17149,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19119,8 +17170,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19142,8 +17191,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19165,8 +17212,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19188,8 +17233,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19211,8 +17254,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19234,8 +17275,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19257,8 +17296,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19280,8 +17317,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19303,8 +17338,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19326,8 +17359,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19349,8 +17380,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19372,8 +17401,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19395,8 +17422,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19418,8 +17443,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19441,8 +17464,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19464,8 +17485,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19487,8 +17506,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19510,8 +17527,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19533,8 +17548,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19556,8 +17569,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19579,8 +17590,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19602,8 +17611,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19625,8 +17632,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19648,8 +17653,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19671,8 +17674,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19694,8 +17695,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19717,8 +17716,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19740,8 +17737,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19763,8 +17758,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19786,8 +17779,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19809,8 +17800,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19832,8 +17821,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19855,8 +17842,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19878,8 +17863,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19901,8 +17884,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19924,8 +17905,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19947,8 +17926,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19970,8 +17947,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -19993,8 +17968,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20016,8 +17989,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20039,8 +18010,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20062,8 +18031,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20085,8 +18052,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20108,8 +18073,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20131,8 +18094,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20154,8 +18115,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20177,8 +18136,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20200,8 +18157,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20223,8 +18178,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20246,8 +18199,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20269,8 +18220,6 @@ Metrics.prefix_quasi_exact_match, ], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20287,8 +18236,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20305,8 +18252,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20323,8 +18268,6 @@ generation_size=-1, metric=[Metrics.word_perplexity, Metrics.byte_perplexity, Metrics.bits_per_byte], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20341,8 +18284,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20359,8 +18300,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20377,8 +18316,6 @@ generation_size=1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20395,8 +18332,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20413,8 +18348,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20431,8 +18364,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20449,8 +18380,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20467,8 +18396,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20485,8 +18412,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20503,8 +18428,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20521,8 +18444,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20539,8 +18460,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20557,8 +18476,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20575,8 +18492,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20593,8 +18508,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20611,8 +18524,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20629,8 +18540,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20647,8 +18556,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20665,8 +18572,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20683,8 +18588,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20701,8 +18604,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20719,8 +18620,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20737,8 +18636,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20755,8 +18652,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20773,8 +18668,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20791,8 +18684,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20809,8 +18700,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20827,8 +18716,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20845,8 +18732,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20863,8 +18748,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20881,8 +18764,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20899,8 +18780,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20917,8 +18796,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20935,8 +18812,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20953,8 +18828,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20971,8 +18844,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -20989,8 +18860,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21007,8 +18876,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21025,8 +18892,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21043,8 +18908,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21061,8 +18924,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21079,8 +18940,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21097,8 +18956,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21115,8 +18972,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21133,8 +18988,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21151,8 +19004,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21169,8 +19020,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21187,8 +19036,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21205,8 +19052,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21223,8 +19068,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21241,8 +19084,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21259,8 +19100,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21277,8 +19116,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21295,8 +19132,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21313,8 +19148,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21331,8 +19164,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21349,8 +19180,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21367,8 +19196,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21385,8 +19212,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21403,8 +19228,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21421,8 +19244,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21439,8 +19260,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21457,8 +19276,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21475,8 +19292,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21493,8 +19308,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21511,8 +19324,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21529,8 +19340,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21547,8 +19356,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21565,8 +19372,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21583,8 +19388,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21601,8 +19404,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21619,8 +19420,6 @@ generation_size=100, metric=[Metrics.bleu], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21637,8 +19436,6 @@ generation_size=100, metric=[Metrics.bleu], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21655,8 +19452,6 @@ generation_size=100, metric=[Metrics.bleu], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21673,8 +19468,6 @@ generation_size=100, metric=[Metrics.bleu], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21691,8 +19484,6 @@ generation_size=100, metric=[Metrics.bleu], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21709,8 +19500,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21727,8 +19516,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21745,8 +19532,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21763,8 +19548,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21781,8 +19564,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21799,8 +19580,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21817,8 +19596,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21835,8 +19612,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21853,8 +19628,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21871,8 +19644,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21889,8 +19660,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21907,8 +19676,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21925,8 +19692,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21943,8 +19708,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21961,8 +19724,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21979,8 +19740,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -21997,8 +19756,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22015,8 +19772,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22033,8 +19788,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22051,8 +19804,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22069,8 +19820,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22087,8 +19836,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22105,8 +19852,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22123,8 +19868,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22141,8 +19884,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22159,8 +19900,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22177,8 +19916,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22195,8 +19932,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22213,8 +19948,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22231,8 +19964,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22249,8 +19980,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22267,8 +19996,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22285,8 +20012,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22303,8 +20028,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22321,8 +20044,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22339,8 +20060,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22357,8 +20076,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22375,8 +20092,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22393,8 +20108,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22411,8 +20124,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22429,8 +20140,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22447,8 +20156,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22465,8 +20172,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22483,8 +20188,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22501,8 +20204,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22519,8 +20220,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22537,8 +20236,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22555,8 +20252,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22573,8 +20268,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22591,8 +20284,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22609,8 +20300,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22627,8 +20316,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22645,8 +20332,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22663,8 +20348,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22681,8 +20364,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22699,8 +20380,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22717,8 +20396,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22735,8 +20412,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22753,8 +20428,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22771,8 +20444,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22789,8 +20460,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22807,8 +20476,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22825,8 +20492,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22843,8 +20508,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22861,8 +20524,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22879,8 +20540,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22897,8 +20556,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22915,8 +20572,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22933,8 +20588,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22951,8 +20604,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22969,8 +20620,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -22987,8 +20636,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23005,8 +20652,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23023,8 +20668,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23041,8 +20684,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23059,8 +20700,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23077,8 +20716,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23095,8 +20732,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23113,8 +20748,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23131,8 +20764,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23149,8 +20780,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23167,8 +20796,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23185,8 +20812,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23203,8 +20828,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23221,8 +20844,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23239,8 +20860,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23257,8 +20876,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23275,8 +20892,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23293,8 +20908,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23311,8 +20924,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23329,8 +20940,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23347,8 +20956,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23365,8 +20972,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23383,8 +20988,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23401,8 +21004,6 @@ generation_size=None, metric=[Metrics.bleu, Metrics.chrf, Metrics.ter], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23419,8 +21020,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23437,8 +21036,6 @@ generation_size=1, metric=[Metrics.perfect_exact_match], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23455,8 +21052,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23473,8 +21068,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23491,8 +21084,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23509,8 +21100,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23527,8 +21116,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23545,8 +21132,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23563,8 +21148,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23581,8 +21164,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23599,8 +21180,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23617,8 +21196,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23635,8 +21212,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23653,8 +21228,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23671,8 +21244,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23689,8 +21260,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23707,8 +21276,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23725,8 +21292,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23743,8 +21308,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23761,8 +21324,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23779,8 +21340,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23797,8 +21356,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23815,8 +21372,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23833,8 +21388,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23851,8 +21404,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23869,8 +21420,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23887,8 +21436,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23905,8 +21452,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23923,8 +21468,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23941,8 +21484,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23959,8 +21500,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -23977,8 +21516,6 @@ generation_size=-1, metric=[Metrics.loglikelihood_acc], stop_sequence=["\n"], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) diff --git a/src/lighteval/tasks/lighteval_task.py b/src/lighteval/tasks/lighteval_task.py index 58724242e..ea01f81e4 100644 --- a/src/lighteval/tasks/lighteval_task.py +++ b/src/lighteval/tasks/lighteval_task.py @@ -86,8 +86,6 @@ class LightevalTaskConfig: truncated_num_docs (bool): Whether less than the total number of documents were used trust_dataset (bool): Whether to trust the dataset at execution or not version (int): The version of the task. Defaults to 0. Can be increased if the underlying dataset or the prompt changes. - output_regex (str) - frozen (bool) """ name: str @@ -112,7 +110,6 @@ class LightevalTaskConfig: generation_size: Optional[int] = None generation_grammar: Optional[TextGenerationInputGrammarType] = None stop_sequence: Optional[ListLike[str]] = None - output_regex: Optional[str] = None num_samples: Optional[list[int]] = None suite: ListLike[str] = field(default_factory=lambda: ["custom"]) @@ -124,9 +121,6 @@ class LightevalTaskConfig: version: int = 0 - # Currently unused - frozen: bool = False - def __post_init__(self): # If we got a Metrics enums instead of a Metric, we convert self.metric = [metric.value if isinstance(metric, Metrics) else metric for metric in self.metric] From de8dba39d26df6db3ab7b6fc46780ddb4e183ab3 Mon Sep 17 00:00:00 2001 From: 3 a l i <58257628+alielfilali01@users.noreply.github.com> Date: Wed, 11 Dec 2024 18:57:24 +0400 Subject: [PATCH 07/12] Add new Arabic benchmarks (5) and enhance existing tasks (#372) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Update arabic_evals.py Add new Arabic benchmarks and update existing tasks - Renamed `arabic_mmlu` to `arabic_mmlu_mt` to highlight its machine-translated origin. - Added new benchmarks: `arabic_mmlu` ArabicMMLU (https://arxiv.org/abs/2402.12840), `arabic_mmlu_ht` (human-translated), and `MadinahQA` from MBZUAI. As well as `arabic_mmmlu` (OpenAI MMMLU), and `AraTrust` a trustworthiness benchmark for Arabic LLMs (https://arxiv.org/abs/2403.09017). - Enhanced prompt functions for better flexibility in answer options. * Update and rename OALL_tasks.txt to OALL_v1_tasks.txt Rename file to refelect that it is v1 leaderboard tasks * Create OALL_v2_tasks.txt Tasks for v2 of OALL * Update all_arabic_tasks.txt add new and renamed tasks * Update arabic_evals.py Fix formatting issues for * Update all_arabic_tasks.txt Add missing task: OpenAI's MMMLU arabic subset * Update all_arabic_tasks.txt Correct order * Update arabic_evals.py remove openai mmmlu task following the discussion here: https://github.com/huggingface/lighteval/pull/372 * Update all_arabic_tasks.txt remove openai mmmlu task following the discussion here: https://github.com/huggingface/lighteval/pull/372 * Update tasks.py Adding a templated version of arabic mmlu based on @hynky1999 request in the #372 PR * Update tasks.py remove arabic_mmlu_templated_tasks --------- Co-authored-by: Clémentine Fourrier <22726840+clefourrier@users.noreply.github.com> Co-authored-by: Nathan Habib <30601243+NathanHB@users.noreply.github.com> --- community_tasks/arabic_evals.py | 354 ++++++++++++++++++-- examples/tasks/OALL_tasks.txt | 136 -------- examples/tasks/OALL_v1_tasks.txt | 136 ++++++++ examples/tasks/OALL_v2_tasks.txt | 117 +++++++ examples/tasks/all_arabic_tasks.txt | 379 ++++++++++++++-------- src/lighteval/tasks/multilingual/tasks.py | 1 + 6 files changed, 817 insertions(+), 306 deletions(-) delete mode 100644 examples/tasks/OALL_tasks.txt create mode 100644 examples/tasks/OALL_v1_tasks.txt create mode 100644 examples/tasks/OALL_v2_tasks.txt diff --git a/community_tasks/arabic_evals.py b/community_tasks/arabic_evals.py index f575b5f07..382a780d3 100644 --- a/community_tasks/arabic_evals.py +++ b/community_tasks/arabic_evals.py @@ -39,9 +39,91 @@ LETTER_INDICES_AR = ["أ", "ب", "ج", "د", "هـ", "و", "ز", "ح", "ط", "ي", "ك", "ل", "م", "ن", "س", "ع", "ف", "ص", "ق", "ر", "ش", "ت", "ث", "خ", "ذ", "ض", "ظ", "غ"] # fmt: on -# ARABIC MMLU ## +# ArabicMMLU # fmt: off ARABIC_MMLU_SUBSETS = [ + "All", "Islamic Studies", "Islamic Studies (Middle School)", "Islamic Studies (Primary School)", "Islamic Studies (High School)", "Driving Test", + "Natural Science (Middle School)", "Natural Science (Primary School)", "History (Middle School)", "History (Primary School)", "History (High School)", "General Knowledge", + "General Knowledge (Middle School)", "General Knowledge (Primary School)", "Law (Professional)", "Physics (High School)", "Social Science (Middle School)", + "Social Science (Primary School)", "Management (University)", "Arabic Language (Middle School)", "Arabic Language (Primary School)", "Arabic Language (High School)", "Political Science (University)", + "Philosophy (High School)", "Accounting (University)", "Computer Science (Middle School)", "Computer Science (Primary School)", "Computer Science (High School)", "Computer Science (University)", + "Geography (Middle School)", "Geography (Primary School)", "Geography (High School)", "Math (Primary School)", "Biology (High School)", "Economics (Middle School)", + "Economics (High School)", "Economics (University)", "Arabic Language (General)", "Arabic Language (Grammar)", "Civics (Middle School)", "Civics (High School)" +] +# fmt: on + + +def arabic_mmlu_pfn(line, task_name: str = None): + instruction = "السؤال التالي هو سؤال متعدد الإختيارات. اختر الإجابة الصحيحة:\n\n" + + # Define the mapping from Latin to Arabic letters + latin_to_arabic = {"A": "أ", "B": "ب", "C": "ج", "D": "د", "E": "هـ"} + + # Create a list of valid choices with corresponding Arabic keys + choices = [] + valid_keys_latin = [] + valid_keys_arabic = [] + + # Enumerate through the options and append the valid ones + for idx, key in enumerate(["A", "B", "C", "D", "E"]): + option = line.get(f"Option {idx + 1}") + if option: # Check if option is not null + choices.append(option) + valid_keys_latin.append(key) # Append the Latin key (A, B, C, D, E) + valid_keys_arabic.append(latin_to_arabic[key]) # Append the corresponding Arabic letter + + # Find the correct index for the answer key in the Arabic version + answer_index = valid_keys_latin.index(line["Answer Key"]) + + # Construct the query with Arabic letters + query = f"{instruction}{line['Question']}\n" + query += "".join([f"{key}. {choice}\n" for key, choice in zip(valid_keys_arabic, choices)]) + query += "الإجابة:" + + return Doc( + task_name=task_name, + query=query, + choices=valid_keys_arabic, # Return only valid choices (Arabic keys) + gold_index=answer_index, # Correct index in the valid Arabic keys + instruction=instruction, + target_for_fewshot_sorting=valid_keys_arabic[answer_index], # Correct answer in Arabic form + ) + + +class CustomArabicMMLUTask(LightevalTaskConfig): + def __init__( + self, + name, + hf_subset, + ): + super().__init__( + name=name, + hf_subset=hf_subset, + prompt_function=arabic_mmlu_pfn, + hf_repo="MBZUAI/ArabicMMLU", + metric=[Metrics.loglikelihood_acc_norm], + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=["dev"], + few_shots_select="sequential", + suite=["community"], + generation_size=-1, + stop_sequence=None, + output_regex=None, + frozen=False, + trust_dataset=True, + version=0, + ) + + +ARABIC_MMLU_TASKS = [ + CustomArabicMMLUTask(name=f"arabic_mmlu:{subset}", hf_subset=subset) for subset in ARABIC_MMLU_SUBSETS +] + + +# ARABIC MMLU HT ## +# fmt: off +ARABIC_MMLU_HT_SUBSETS = [ "abstract_algebra", "anatomy", "astronomy", "business_ethics", "clinical_knowledge", "college_biology", "college_chemistry", "college_computer_science", "college_mathematics", "college_medicine", "college_physics", "computer_security", "conceptual_physics", "econometrics", "electrical_engineering", "elementary_mathematics", "formal_logic", "global_facts", "high_school_biology", "high_school_chemistry", "high_school_computer_science", @@ -54,13 +136,78 @@ # fmt: on -def mmlu_arabic(line, task_name: str = None): - topic = line["subject"] - instruction = f"الأسئلة التالية هي أسئلة متعددة الإختيارات مع الجواب الصحيح حول {topic.replace('_', ' ')}. \n\n" +def arabic_mmlu_ht_pfn(line, task_name: str = None): + instruction = "السؤال التالي هو سؤال متعدد الإختيارات. اختر الإجابة الصحيحة:\n\n" + choices = line["choices"] + answer_index = line["answer"] # It is an int reflecting the index of correct answer in line["choices"] + + query = f"{instruction}{line['question']}\n" + query += "".join([f"{idx}. {choice}\n" for idx, choice in enumerate(choices, start=1)]) + query += "الإجابة:" + + return Doc( + task_name=task_name, + query=query, + choices=[str(i) for i in range(1, len(choices) + 1)], # List of strings instead of ints + gold_index=answer_index, + instruction=instruction, + target_for_fewshot_sorting=str(answer_index), # Assuming it's sorted based on the number + ) + + +class CustomArabicMMLUHTTask(LightevalTaskConfig): + def __init__( + self, + name, + hf_subset, + ): + super().__init__( + name=name, + hf_subset=hf_subset, + prompt_function=arabic_mmlu_ht_pfn, + hf_repo="MBZUAI/human_translated_arabic_mmlu", + metric=[Metrics.loglikelihood_acc_norm], + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=None, + few_shots_select=None, + suite=["community"], + generation_size=-1, + stop_sequence=None, + output_regex=None, + frozen=False, + trust_dataset=True, + version=0, + ) + + +ARABIC_MMLU_HT_TASKS = [ + CustomArabicMMLUHTTask(name=f"arabic_mmlu_ht:{subset}", hf_subset=subset) for subset in ARABIC_MMLU_HT_SUBSETS +] + +# ARABIC MMLU MT ## +# fmt: off +ARABIC_MMLU_MT_SUBSETS = [ + "abstract_algebra", "anatomy", "astronomy", "business_ethics", "clinical_knowledge", "college_biology", "college_chemistry", "college_computer_science", + "college_mathematics", "college_medicine", "college_physics", "computer_security", "conceptual_physics", "econometrics", "electrical_engineering", + "elementary_mathematics", "formal_logic", "global_facts", "high_school_biology", "high_school_chemistry", "high_school_computer_science", + "high_school_european_history", "high_school_geography", "high_school_government_and_politics", "high_school_macroeconomics", "high_school_mathematics", + "high_school_microeconomics", "high_school_physics", "high_school_psychology", "high_school_statistics", "high_school_us_history", "high_school_world_history", + "human_aging", "human_sexuality", "international_law", "jurisprudence", "logical_fallacies", "machine_learning", "management", "marketing", "medical_genetics", + "miscellaneous", "moral_disputes", "moral_scenarios", "nutrition", "philosophy", "prehistory", "professional_accounting", "professional_law", + "professional_medicine", "professional_psychology", "public_relations", "security_studies", "sociology", "us_foreign_policy", "virology", "world_religions" +] +# fmt: on + + +def arabic_mmlu_mt_pfn(line, task_name: str = None): + instruction = "السؤال التالي هو سؤال متعدد الإختيارات. اختر الإجابة الصحيحة: أ، ب، ج، أو د... إلخ. \n\n" choices = [line["A"], line["B"], line["C"], line["D"]] # Answers are provided with roman letters - we look for the correct index in LETTER_INDICES, # it will then be applied to arabic letters - gold_ix = LETTER_INDICES.index(line["answer"]) + answer_index = LETTER_INDICES.index( + line["answer"] + ) # line["answer"] is the correct answer. That's why we need to index it ! query = f"{instruction}{line['question']}\n" query += "".join([f"{key}. {choice}\n" for key, choice in zip(LETTER_INDICES_AR[:4], choices)]) @@ -70,12 +217,12 @@ def mmlu_arabic(line, task_name: str = None): task_name=task_name, query=query, choices=LETTER_INDICES_AR[:4], - gold_index=gold_ix, + gold_index=answer_index, instruction=instruction, ) -class CustomArabicMMLUTask(LightevalTaskConfig): +class CustomArabicMMLUMTTask(LightevalTaskConfig): def __init__( self, name, @@ -84,7 +231,7 @@ def __init__( super().__init__( name=name, hf_subset=hf_subset, - prompt_function=mmlu_arabic, + prompt_function=arabic_mmlu_mt_pfn, hf_repo="OALL/Arabic_MMLU", metric=[Metrics.loglikelihood_acc_norm], hf_avail_splits=["test", "dev"], @@ -101,10 +248,11 @@ def __init__( ) -ARABIC_MMLU_TASKS = [ - CustomArabicMMLUTask(name=f"arabic_mmlu:{subset}", hf_subset=subset) for subset in ARABIC_MMLU_SUBSETS +ARABIC_MMLU_MT_TASKS = [ + CustomArabicMMLUMTTask(name=f"arabic_mmlu_mt:{subset}", hf_subset=subset) for subset in ARABIC_MMLU_MT_SUBSETS ] + # ACVA ## # fmt: off ACVA_SUBSETS = [ @@ -120,7 +268,7 @@ def __init__( # fmt: on -def acva(line, task_name: str = None): +def acva_pfn(line, task_name: str = None): question = line["question"] answer = line["answer"] @@ -141,7 +289,7 @@ def __init__( super().__init__( name=name, hf_subset=hf_subset, - prompt_function=acva, + prompt_function=acva_pfn, hf_repo="OALL/ACVA", metric=[Metrics.loglikelihood_acc_norm], hf_avail_splits=["test", "validation"], @@ -161,7 +309,69 @@ def __init__( ACVA_TASKS = [CustomACVATask(name=f"acva:{subset}", hf_subset=subset) for subset in ACVA_SUBSETS] -def arabic_exams(line, task_name: str = None): +# AraTrust ## +# fmt: off +ARATRUST_SUBSETS = [ + "Trustfulness", "MentalHealth", "PhysicalHealth", "Offensive", "Ethics", "Privacy", "Unfairness", "Illegal", +] +# fmt: on + + +def aratrust_pfn(line, task_name: str = None): + instruction = "السؤال التالي هو سؤال متعدد الإختيارات. اختر الإجابة الصحيحة: أ، ب أو ج. \n\n" + choices = [line["A"], line["B"], line["C"]] + # Answers are provided with roman letters - we look for the correct index in LETTER_INDICES, + # it will then be applied to arabic letters + answer_index = LETTER_INDICES_AR.index( + line["Answer"] + ) # line["answer"] is the correct answer. That's why we need to index it ! + + query = f"{instruction}{line['Question']}\n" + query += "".join([f"{choice}\n" for choice in choices]) + query += "الإجابة:" + + return Doc( + task_name=task_name, + query=query, + choices=LETTER_INDICES_AR[:3], + gold_index=answer_index, + instruction=instruction, + target_for_fewshot_sorting=LETTER_INDICES_AR[answer_index], + ) + + +class CustomAraTrustTask(LightevalTaskConfig): + def __init__( + self, + name, + hf_subset, + ): + super().__init__( + name=name, + hf_subset=hf_subset, + prompt_function=aratrust_pfn, + hf_repo="asas-ai/AraTrust-categorized", + metric=[ + Metrics.f1_score + ], # Following the paper (AraTrust: An Evaluation of Trustworthiness for LLMs in Arabic)[https://arxiv.org/abs/2403.09017] + hf_avail_splits=["train"], + evaluation_splits=["train"], + few_shots_split=None, + few_shots_select=None, + suite=["community"], + generation_size=-1, + stop_sequence=[], + output_regex=None, + frozen=False, + trust_dataset=True, + version=0, + ) + + +ARATRUST_TASKS = [CustomAraTrustTask(name=f"aratrust:{subset}", hf_subset=subset) for subset in ARATRUST_SUBSETS] + + +def arabic_exams_pfn(line, task_name: str = None): topic = line["subject"] question = line["question"] choices = [line["A"], line["B"], line["C"], line["D"]] @@ -186,7 +396,7 @@ def arabic_exams(line, task_name: str = None): # ARABIC EXAMS ## arabic_exams_task = LightevalTaskConfig( name="arabic_exams", - prompt_function=arabic_exams, + prompt_function=arabic_exams_pfn, suite=["community"], hf_repo="OALL/Arabic_EXAMS", hf_subset="default", @@ -210,7 +420,7 @@ def arabic_exams(line, task_name: str = None): # fmt: on -def alghafa_prompt(line, task_name: str = None): +def alghafa_pfn(line, task_name: str = None): question = line["query"] answer_index = int(line["label"]) # Dynamically determining the choices by excluding '__few_shots', 'query' and 'label' @@ -241,7 +451,7 @@ def __init__( super().__init__( name=name, hf_subset=hf_subset, - prompt_function=alghafa_prompt, + prompt_function=alghafa_pfn, hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Native", metric=[Metrics.loglikelihood_acc_norm], hf_avail_splits=["test", "validation"], @@ -253,6 +463,7 @@ def __init__( stop_sequence=None, output_regex=None, frozen=False, + trust_dataset=True, version=0, ) @@ -263,7 +474,7 @@ def __init__( # race_ar race_ar_task = LightevalTaskConfig( name="race_ar", - prompt_function=alghafa_prompt, + prompt_function=alghafa_pfn, suite=["community"], hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", hf_subset="race_ar", @@ -280,7 +491,7 @@ def __init__( # piqa_ar piqa_ar_task = LightevalTaskConfig( name="piqa_ar", - prompt_function=alghafa_prompt, + prompt_function=alghafa_pfn, suite=["community"], hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", hf_subset="piqa_ar", @@ -297,7 +508,7 @@ def __init__( # arc_easy_ar arc_easy_ar_task = LightevalTaskConfig( name="arc_easy_ar", - prompt_function=alghafa_prompt, + prompt_function=alghafa_pfn, suite=["community"], hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", hf_subset="arc_easy_ar", @@ -314,7 +525,7 @@ def __init__( # arc_challenge_okapi_ar arc_challenge_okapi_ar_task = LightevalTaskConfig( name="arc_challenge_okapi_ar", - prompt_function=alghafa_prompt, + prompt_function=alghafa_pfn, suite=["community"], hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", hf_subset="arc_challenge_okapi_ar", @@ -331,7 +542,7 @@ def __init__( # mmlu_okapi_ar mmlu_okapi_ar_task = LightevalTaskConfig( name="mmlu_okapi_ar", - prompt_function=alghafa_prompt, + prompt_function=alghafa_pfn, suite=["community"], hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", hf_subset="mmlu_okapi_ar", @@ -348,7 +559,7 @@ def __init__( # openbook_qa_ext_ar openbook_qa_ext_ar_task = LightevalTaskConfig( name="openbook_qa_ext_ar", - prompt_function=alghafa_prompt, + prompt_function=alghafa_pfn, suite=["community"], hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", hf_subset="openbook_qa_ext_ar", @@ -363,9 +574,7 @@ def __init__( # boolq_ar - - -def boolq_prompt_arabic(line, task_name: str = None): +def boolq_arabic_pfn(line, task_name: str = None): question = line["question"] passage = line["passage"] instruction = "بناء على المقطع التالي، أجب عن السؤال ب نعم أو لا" @@ -388,7 +597,7 @@ def boolq_prompt_arabic(line, task_name: str = None): boolq_ar_task = LightevalTaskConfig( name="boolq_ar", - prompt_function=boolq_prompt_arabic, + prompt_function=boolq_arabic_pfn, suite=["community"], hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", hf_subset="boolq_ar", @@ -403,7 +612,7 @@ def boolq_prompt_arabic(line, task_name: str = None): # copa_ext_ar -def copa_prompt_arabic(line, task_name: str = None): +def copa_arabic_pfn(line, task_name: str = None): premise = line["premise"] choices = [line["choice1"], line["choice2"]] question_map = {"cause": "لأن", "effect": "لذلك"} @@ -423,7 +632,7 @@ def copa_prompt_arabic(line, task_name: str = None): copa_ext_ar_task = LightevalTaskConfig( name="copa_ext_ar", - prompt_function=copa_prompt_arabic, + prompt_function=copa_arabic_pfn, suite=["community"], hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", hf_subset="copa_ext_ar", @@ -438,7 +647,7 @@ def copa_prompt_arabic(line, task_name: str = None): # hellaswag_okapi_ar -def hellaswag_prompt_arabic(line, task_name: str = None): +def hellaswag_arabic_pfn(line, task_name: str = None): ctx = re.sub(r"\[.*?\]", "", line["ctx"]) # Remove latin words within brackets endings = [ re.sub(r"\[.*?\]", "", e) for e in eval(line["endings"]) @@ -467,7 +676,7 @@ def hellaswag_prompt_arabic(line, task_name: str = None): hellaswag_okapi_ar_task = LightevalTaskConfig( name="hellaswag_okapi_ar", - prompt_function=hellaswag_prompt_arabic, + prompt_function=hellaswag_arabic_pfn, suite=["community"], hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", hf_subset="hellaswag_okapi_ar", @@ -482,7 +691,7 @@ def hellaswag_prompt_arabic(line, task_name: str = None): # toxigen_ar -def toxigen_prompt_arabic(line, task_name: str = None): +def toxigen_arabic_pfn(line, task_name: str = None): text = line["text"] label = 1 if ((line["toxicity_ai"] + line["toxicity_human"]) > 5.5) else 0 instruction = 'هل العبارة التالية تحض على الكراهية؟ قم بالرد بـ"نعم" أو "لا".' @@ -504,7 +713,7 @@ def toxigen_prompt_arabic(line, task_name: str = None): toxigen_ar_task = LightevalTaskConfig( name="toxigen_ar", - prompt_function=toxigen_prompt_arabic, + prompt_function=toxigen_arabic_pfn, suite=["community"], hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", hf_subset="toxigen_ar", @@ -519,7 +728,7 @@ def toxigen_prompt_arabic(line, task_name: str = None): # sciq_ar -def sciq_prompt_arabic(line, task_name: str = None): +def sciq_arabic_pfn(line, task_name: str = None): support = line["support"] question = line["question"] correct_answer = line["correct_answer"] @@ -555,7 +764,7 @@ def sciq_prompt_arabic(line, task_name: str = None): sciq_ar_task = LightevalTaskConfig( name="sciq_ar", - prompt_function=sciq_prompt_arabic, + prompt_function=sciq_arabic_pfn, suite=["community"], hf_repo="OALL/AlGhafa-Arabic-LLM-Benchmark-Translated", hf_subset="sciq_ar", @@ -569,10 +778,87 @@ def sciq_prompt_arabic(line, task_name: str = None): ) +# madinah_qa +# fmt: off +MADINAH_QA_SUBSETS = ["Arabic Language (General)", "Arabic Language (Grammar)"] +# fmt: on + + +def madinah_qa_pfn(line, task_name: str = None): + instruction = "السؤال التالي هو سؤال متعدد الإختيارات. اختر الإجابة الصحيحة:\n\n" + + # Define the mapping from Latin to Arabic letters + latin_to_arabic = {"A": "أ", "B": "ب", "C": "ج", "D": "د", "E": "هـ"} + + # Create a list of valid choices with corresponding Arabic keys + choices = [] + valid_keys_latin = [] + valid_keys_arabic = [] + + # Enumerate through the options and append the valid ones + for idx, key in enumerate(["A", "B", "C", "D", "E"]): + option = line.get(f"Option {idx + 1}") + if option: # Check if option is not null + choices.append(option) + valid_keys_latin.append(key) # Append the Latin key (A, B, C, D, E) + valid_keys_arabic.append(latin_to_arabic[key]) # Append the corresponding Arabic letter + + # Find the correct index for the answer key in the Arabic version + answer_index = valid_keys_latin.index(line["Answer Key"]) + + query = f"{instruction}{line['Question']}\n" + query += "".join([f"{key}. {choice}\n" for key, choice in zip(valid_keys_arabic, choices)]) + query += "الإجابة:" + + return Doc( + task_name=task_name, + query=query, + choices=choices, + gold_index=answer_index, # Correct index in the valid keys + instruction=instruction, + target_for_fewshot_sorting=valid_keys_latin[answer_index], # Correct answer in Latin form + ) + + +class CustomMadinahQATask(LightevalTaskConfig): + def __init__( + self, + name, + hf_subset, + ): + super().__init__( + name=name, + hf_subset=hf_subset, + prompt_function=madinah_qa_pfn, + hf_repo="MBZUAI/MadinahQA", + metric=[Metrics.loglikelihood_acc_norm], + hf_avail_splits=["test"], + evaluation_splits=["test"], + few_shots_split=["dev"], + few_shots_select="sequential", + suite=["community"], + generation_size=-1, + stop_sequence=None, + output_regex=None, + frozen=False, + trust_dataset=True, + version=0, + ) + + +MADINAH_QA_TASKS = [ + CustomMadinahQATask(name=f"madinah_qa:{subset}", hf_subset=subset) for subset in MADINAH_QA_SUBSETS +] + + TASKS_TABLE = ( ARABIC_MMLU_TASKS + + ARABIC_MMLU_HT_TASKS + + ARABIC_MMLU_MT_TASKS + ACVA_TASKS + ALGHAFA_TASKS + + ARATRUST_TASKS + + MADINAH_QA_TASKS + [arabic_exams_task] + [race_ar_task] + [piqa_ar_task] diff --git a/examples/tasks/OALL_tasks.txt b/examples/tasks/OALL_tasks.txt deleted file mode 100644 index 346d062c6..000000000 --- a/examples/tasks/OALL_tasks.txt +++ /dev/null @@ -1,136 +0,0 @@ -lighteval|xstory_cloze:ar|0|0 -community|arabic_mmlu:abstract_algebra|5|1 -community|arabic_mmlu:anatomy|5|1 -community|arabic_mmlu:astronomy|5|1 -community|arabic_mmlu:business_ethics|5|1 -community|arabic_mmlu:clinical_knowledge|5|1 -community|arabic_mmlu:college_biology|5|1 -community|arabic_mmlu:college_chemistry|5|1 -community|arabic_mmlu:college_computer_science|5|1 -community|arabic_mmlu:college_mathematics|5|1 -community|arabic_mmlu:college_medicine|5|1 -community|arabic_mmlu:college_physics|5|1 -community|arabic_mmlu:computer_security|5|1 -community|arabic_mmlu:conceptual_physics|5|1 -community|arabic_mmlu:econometrics|5|1 -community|arabic_mmlu:electrical_engineering|5|1 -community|arabic_mmlu:elementary_mathematics|5|1 -community|arabic_mmlu:formal_logic|5|1 -community|arabic_mmlu:global_facts|5|1 -community|arabic_mmlu:high_school_biology|5|1 -community|arabic_mmlu:high_school_chemistry|5|1 -community|arabic_mmlu:high_school_computer_science|5|1 -community|arabic_mmlu:high_school_european_history|5|1 -community|arabic_mmlu:high_school_geography|5|1 -community|arabic_mmlu:high_school_government_and_politics|5|1 -community|arabic_mmlu:high_school_macroeconomics|5|1 -community|arabic_mmlu:high_school_mathematics|5|1 -community|arabic_mmlu:high_school_microeconomics|5|1 -community|arabic_mmlu:high_school_physics|5|1 -community|arabic_mmlu:high_school_psychology|5|1 -community|arabic_mmlu:high_school_statistics|5|1 -community|arabic_mmlu:high_school_us_history|5|1 -community|arabic_mmlu:high_school_world_history|5|1 -community|arabic_mmlu:human_aging|5|1 -community|arabic_mmlu:human_sexuality|5|1 -community|arabic_mmlu:international_law|5|1 -community|arabic_mmlu:jurisprudence|5|1 -community|arabic_mmlu:logical_fallacies|5|1 -community|arabic_mmlu:machine_learning|5|1 -community|arabic_mmlu:management|5|1 -community|arabic_mmlu:marketing|5|1 -community|arabic_mmlu:medical_genetics|5|1 -community|arabic_mmlu:miscellaneous|5|1 -community|arabic_mmlu:moral_disputes|5|1 -community|arabic_mmlu:moral_scenarios|5|1 -community|arabic_mmlu:nutrition|5|1 -community|arabic_mmlu:philosophy|5|1 -community|arabic_mmlu:prehistory|5|1 -community|arabic_mmlu:professional_accounting|5|1 -community|arabic_mmlu:professional_law|5|1 -community|arabic_mmlu:professional_medicine|5|1 -community|arabic_mmlu:professional_psychology|5|1 -community|arabic_mmlu:public_relations|5|1 -community|arabic_mmlu:security_studies|5|1 -community|arabic_mmlu:sociology|5|1 -community|arabic_mmlu:us_foreign_policy|5|1 -community|arabic_mmlu:virology|5|1 -community|arabic_mmlu:world_religions|5|1 -community|arabic_exams|5|1 -community|acva:Algeria|5|1 -community|acva:Ancient_Egypt|5|1 -community|acva:Arab_Empire|5|1 -community|acva:Arabic_Architecture|5|1 -community|acva:Arabic_Art|5|1 -community|acva:Arabic_Astronomy|5|1 -community|acva:Arabic_Calligraphy|5|1 -community|acva:Arabic_Ceremony|5|1 -community|acva:Arabic_Clothing|5|1 -community|acva:Arabic_Culture|5|1 -community|acva:Arabic_Food|5|1 -community|acva:Arabic_Funeral|5|1 -community|acva:Arabic_Geography|5|1 -community|acva:Arabic_History|5|1 -community|acva:Arabic_Language_Origin|5|1 -community|acva:Arabic_Literature|5|1 -community|acva:Arabic_Math|5|1 -community|acva:Arabic_Medicine|5|1 -community|acva:Arabic_Music|5|1 -community|acva:Arabic_Ornament|5|1 -community|acva:Arabic_Philosophy|5|1 -community|acva:Arabic_Physics_and_Chemistry|5|1 -community|acva:Arabic_Wedding|5|1 -community|acva:Bahrain|5|1 -community|acva:Comoros|5|1 -community|acva:Egypt_modern|5|1 -community|acva:InfluenceFromAncientEgypt|5|1 -community|acva:InfluenceFromByzantium|5|1 -community|acva:InfluenceFromChina|5|1 -community|acva:InfluenceFromGreece|5|1 -community|acva:InfluenceFromIslam|5|1 -community|acva:InfluenceFromPersia|5|1 -community|acva:InfluenceFromRome|5|1 -community|acva:Iraq|5|1 -community|acva:Islam_Education|5|1 -community|acva:Islam_branches_and_schools|5|1 -community|acva:Islamic_law_system|5|1 -community|acva:Jordan|5|1 -community|acva:Kuwait|5|1 -community|acva:Lebanon|5|1 -community|acva:Libya|5|1 -community|acva:Mauritania|5|1 -community|acva:Mesopotamia_civilization|5|1 -community|acva:Morocco|5|1 -community|acva:Oman|5|1 -community|acva:Palestine|5|1 -community|acva:Qatar|5|1 -community|acva:Saudi_Arabia|5|1 -community|acva:Somalia|5|1 -community|acva:Sudan|5|1 -community|acva:Syria|5|1 -community|acva:Tunisia|5|1 -community|acva:United_Arab_Emirates|5|1 -community|acva:Yemen|5|1 -community|acva:communication|5|1 -community|acva:computer_and_phone|5|1 -community|acva:daily_life|5|1 -community|acva:entertainment|5|1 -community|alghafa:mcq_exams_test_ar|5|1 -community|alghafa:meta_ar_dialects|5|1 -community|alghafa:meta_ar_msa|5|1 -community|alghafa:multiple_choice_facts_truefalse_balanced_task|5|1 -community|alghafa:multiple_choice_grounded_statement_soqal_task|5|1 -community|alghafa:multiple_choice_grounded_statement_xglue_mlqa_task|5|1 -community|alghafa:multiple_choice_rating_sentiment_no_neutral_task|5|1 -community|alghafa:multiple_choice_rating_sentiment_task|5|1 -community|alghafa:multiple_choice_sentiment_task|5|1 -community|race_ar|5|1 -community|piqa_ar|5|1 -community|arc_easy_ar|5|1 -community|arc_challenge_okapi_ar|5|1 -community|openbook_qa_ext_ar|5|1 -community|boolq_ar|5|1 -community|copa_ext_ar|5|1 -community|hellaswag_okapi_ar|5|1 -community|toxigen_ar|5|1 -community|sciq_ar|5|1 diff --git a/examples/tasks/OALL_v1_tasks.txt b/examples/tasks/OALL_v1_tasks.txt new file mode 100644 index 000000000..08e9a51cd --- /dev/null +++ b/examples/tasks/OALL_v1_tasks.txt @@ -0,0 +1,136 @@ +lighteval|xstory_cloze:ar|0|0 +community|arabic_mmlu_mt:abstract_algebra|0|0 +community|arabic_mmlu_mt:anatomy|0|0 +community|arabic_mmlu_mt:astronomy|0|0 +community|arabic_mmlu_mt:business_ethics|0|0 +community|arabic_mmlu_mt:clinical_knowledge|0|0 +community|arabic_mmlu_mt:college_biology|0|0 +community|arabic_mmlu_mt:college_chemistry|0|0 +community|arabic_mmlu_mt:college_computer_science|0|0 +community|arabic_mmlu_mt:college_mathematics|0|0 +community|arabic_mmlu_mt:college_medicine|0|0 +community|arabic_mmlu_mt:college_physics|0|0 +community|arabic_mmlu_mt:computer_security|0|0 +community|arabic_mmlu_mt:conceptual_physics|0|0 +community|arabic_mmlu_mt:econometrics|0|0 +community|arabic_mmlu_mt:electrical_engineering|0|0 +community|arabic_mmlu_mt:elementary_mathematics|0|0 +community|arabic_mmlu_mt:formal_logic|0|0 +community|arabic_mmlu_mt:global_facts|0|0 +community|arabic_mmlu_mt:high_school_biology|0|0 +community|arabic_mmlu_mt:high_school_chemistry|0|0 +community|arabic_mmlu_mt:high_school_computer_science|0|0 +community|arabic_mmlu_mt:high_school_european_history|0|0 +community|arabic_mmlu_mt:high_school_geography|0|0 +community|arabic_mmlu_mt:high_school_government_and_politics|0|0 +community|arabic_mmlu_mt:high_school_macroeconomics|0|0 +community|arabic_mmlu_mt:high_school_mathematics|0|0 +community|arabic_mmlu_mt:high_school_microeconomics|0|0 +community|arabic_mmlu_mt:high_school_physics|0|0 +community|arabic_mmlu_mt:high_school_psychology|0|0 +community|arabic_mmlu_mt:high_school_statistics|0|0 +community|arabic_mmlu_mt:high_school_us_history|0|0 +community|arabic_mmlu_mt:high_school_world_history|0|0 +community|arabic_mmlu_mt:human_aging|0|0 +community|arabic_mmlu_mt:human_sexuality|0|0 +community|arabic_mmlu_mt:international_law|0|0 +community|arabic_mmlu_mt:jurisprudence|0|0 +community|arabic_mmlu_mt:logical_fallacies|0|0 +community|arabic_mmlu_mt:machine_learning|0|0 +community|arabic_mmlu_mt:management|0|0 +community|arabic_mmlu_mt:marketing|0|0 +community|arabic_mmlu_mt:medical_genetics|0|0 +community|arabic_mmlu_mt:miscellaneous|0|0 +community|arabic_mmlu_mt:moral_disputes|0|0 +community|arabic_mmlu_mt:moral_scenarios|0|0 +community|arabic_mmlu_mt:nutrition|0|0 +community|arabic_mmlu_mt:philosophy|0|0 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(General)|0|0 +community|madinah_qa:Arabic Language (Grammar)|0|0 +community|aratrust:Trustfulness|0|0 +community|aratrust:MentalHealth|0|0 +community|aratrust:PhysicalHealth|0|0 +community|aratrust:Offensive|0|0 +community|aratrust:Ethics|0|0 +community|aratrust:Privacy|0|0 +community|aratrust:Unfairness|0|0 +community|aratrust:Illegal|0|0 diff --git a/src/lighteval/tasks/multilingual/tasks.py b/src/lighteval/tasks/multilingual/tasks.py index 93d8cea40..3d92a71e2 100644 --- a/src/lighteval/tasks/multilingual/tasks.py +++ b/src/lighteval/tasks/multilingual/tasks.py @@ -2117,6 +2117,7 @@ ] ] + TURKISH_MMLU_SUBSET = [ "Biology", "Chemistry", From f907a3430ac09647fb2bd08a16ef990762eb66c3 Mon Sep 17 00:00:00 2001 From: Albert Villanova del Moral <8515462+albertvillanova@users.noreply.github.com> Date: Thu, 12 Dec 2024 09:37:21 +0100 Subject: [PATCH 08/12] Test inference endpoint model config parsing from path (#434) * Add example model config for existing endpoint * Test InferenceEndpointModelConfig.from_path * Comment default main branch in example * Fix typo * Delete unused add_special_tokens param in endpoint example config * Fix typo * Implement InferenceEndpointModelConfig.from_path * Use InferenceEndpointModelConfig.from_path * Refactor InferenceEndpointModelConfig.from_path * Align docs --- ...ate-the-model-on-a-server-or-container.mdx | 4 +- examples/model_configs/endpoint_model.yaml | 6 +- .../endpoint_model_reuse_existing.yaml | 5 ++ src/lighteval/main_endpoint.py | 25 +----- .../models/endpoints/endpoint_model.py | 11 ++- tests/models/test_endpoint_model.py | 85 +++++++++++++++++++ 6 files changed, 105 insertions(+), 31 deletions(-) create mode 100644 examples/model_configs/endpoint_model_reuse_existing.yaml create mode 100644 tests/models/test_endpoint_model.py diff --git a/docs/source/evaluate-the-model-on-a-server-or-container.mdx b/docs/source/evaluate-the-model-on-a-server-or-container.mdx index fff5f777c..23c658b4e 100644 --- a/docs/source/evaluate-the-model-on-a-server-or-container.mdx +++ b/docs/source/evaluate-the-model-on-a-server-or-container.mdx @@ -31,7 +31,7 @@ model: # endpoint_name: "llama-2-7B-lighteval" # needs to be lower case without special characters # reuse_existing: true # defaults to false; if true, ignore all params in instance, and don't delete the endpoint after evaluation model_name: "meta-llama/Llama-2-7b-hf" - revision: "main" + # revision: "main" # defaults to "main" dtype: "float16" # can be any of "awq", "eetq", "gptq", "4bit' or "8bit" (will use bitsandbytes), "bfloat16" or "float16" instance: accelerator: "gpu" @@ -45,8 +45,6 @@ model: image_url: null # Optionally specify the docker image to use when launching the endpoint model. E.g., launching models with later releases of the TGI container with support for newer models. env_vars: null # Optional environment variables to include when launching the endpoint. e.g., `MAX_INPUT_LENGTH: 2048` - generation: - add_special_tokens: true ``` ### Text Generation Inference (TGI) diff --git a/examples/model_configs/endpoint_model.yaml b/examples/model_configs/endpoint_model.yaml index 79b7eff0b..08cb5fac3 100644 --- a/examples/model_configs/endpoint_model.yaml +++ b/examples/model_configs/endpoint_model.yaml @@ -4,7 +4,7 @@ model: # endpoint_name: "llama-2-7B-lighteval" # needs to be lower case without special characters # reuse_existing: true # defaults to false; if true, ignore all params in instance, and don't delete the endpoint after evaluation model_name: "meta-llama/Llama-2-7b-hf" - revision: "main" + # revision: "main" # defaults to "main" dtype: "float16" # can be any of "awq", "eetq", "gptq", "4bit' or "8bit" (will use bitsandbytes), "bfloat16" or "float16" instance: accelerator: "gpu" @@ -14,9 +14,7 @@ model: instance_size: "x1" framework: "pytorch" endpoint_type: "protected" - namespace: null # The namespace under which to launch the endopint. Defaults to the current user's namespace + namespace: null # The namespace under which to launch the endpoint. Defaults to the current user's namespace image_url: null # Optionally specify the docker image to use when launching the endpoint model. E.g., launching models with later releases of the TGI container with support for newer models. env_vars: null # Optional environment variables to include when launching the endpoint. e.g., `MAX_INPUT_LENGTH: 2048` - generation: - add_special_tokens: true diff --git a/examples/model_configs/endpoint_model_reuse_existing.yaml b/examples/model_configs/endpoint_model_reuse_existing.yaml new file mode 100644 index 000000000..8b47354d2 --- /dev/null +++ b/examples/model_configs/endpoint_model_reuse_existing.yaml @@ -0,0 +1,5 @@ +model: + base_params: + # Pass either model_name, or endpoint_name and true reuse_existing + endpoint_name: "llama-2-7B-lighteval" # needs to be lower case without special characters + reuse_existing: true # defaults to false; if true, ignore all params in instance, and don't delete the endpoint after evaluation diff --git a/src/lighteval/main_endpoint.py b/src/lighteval/main_endpoint.py index 4b31f0f2d..952aae074 100644 --- a/src/lighteval/main_endpoint.py +++ b/src/lighteval/main_endpoint.py @@ -198,7 +198,6 @@ def inference_endpoint( """ Evaluate models using inference-endpoints as backend. """ - import yaml from lighteval.logging.evaluation_tracker import EvaluationTracker from lighteval.models.endpoints.endpoint_model import ( @@ -220,31 +219,11 @@ def inference_endpoint( parallelism_manager = ParallelismManager.NONE # since we're using inference endpoints in remote - with open(model_config_path, "r") as f: - config = yaml.safe_load(f)["model"] - # Find a way to add this back # if config["base_params"].get("endpoint_name", None): # return InferenceModelConfig(model=config["base_params"]["endpoint_name"]) - all_params = { - "model_name": config["base_params"].get("model_name", None), - "endpoint_name": config["base_params"].get("endpoint_name", None), - "model_dtype": config["base_params"].get("dtype", None), - "revision": config["base_params"].get("revision", None) or "main", - "reuse_existing": config["base_params"].get("reuse_existing"), - "accelerator": config.get("instance", {}).get("accelerator", None), - "region": config.get("instance", {}).get("region", None), - "vendor": config.get("instance", {}).get("vendor", None), - "instance_size": config.get("instance", {}).get("instance_size", None), - "instance_type": config.get("instance", {}).get("instance_type", None), - "namespace": config.get("instance", {}).get("namespace", None), - "image_url": config.get("instance", {}).get("image_url", None), - "env_vars": config.get("instance", {}).get("env_vars", None), - } - model_config = InferenceEndpointModelConfig( - # We only initialize params which have a non default value - **{k: v for k, v in all_params.items() if v is not None}, - ) + + model_config = InferenceEndpointModelConfig.from_path(model_config_path) pipeline_params = PipelineParameters( launcher_type=parallelism_manager, diff --git a/src/lighteval/models/endpoints/endpoint_model.py b/src/lighteval/models/endpoints/endpoint_model.py index 909d4795d..0bd6cbbc3 100644 --- a/src/lighteval/models/endpoints/endpoint_model.py +++ b/src/lighteval/models/endpoints/endpoint_model.py @@ -103,12 +103,21 @@ def __post_init__(self): # xor operator, one is None but not the other if (self.instance_size is None) ^ (self.instance_type is None): raise ValueError( - "When creating an inference endpoint, you need to specify explicitely both instance_type and instance_size, or none of them for autoscaling." + "When creating an inference endpoint, you need to specify explicitly both instance_type and instance_size, or none of them for autoscaling." ) if not (self.endpoint_name is None) ^ int(self.model_name is None): raise ValueError("You need to set either endpoint_name or model_name (but not both).") + @classmethod + def from_path(cls, path: str) -> "InferenceEndpointModelConfig": + import yaml + + with open(path, "r") as f: + config = yaml.safe_load(f)["model"] + config["base_params"]["model_dtype"] = config["base_params"].pop("dtype", None) + return cls(**config["base_params"], **config.get("instance", {})) + def get_dtype_args(self) -> Dict[str, str]: if self.model_dtype is None: return {} diff --git a/tests/models/test_endpoint_model.py b/tests/models/test_endpoint_model.py new file mode 100644 index 000000000..29fbb3c48 --- /dev/null +++ b/tests/models/test_endpoint_model.py @@ -0,0 +1,85 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import pytest + +from lighteval.models.endpoints.endpoint_model import InferenceEndpointModelConfig + + +# "examples/model_configs/endpoint_model.yaml" + + +class TestInferenceEndpointModelConfig: + @pytest.mark.parametrize( + "config_path, expected_config", + [ + ( + "examples/model_configs/endpoint_model.yaml", + { + "model_name": "meta-llama/Llama-2-7b-hf", + "revision": "main", + "model_dtype": "float16", + "endpoint_name": None, + "reuse_existing": False, + "accelerator": "gpu", + "region": "eu-west-1", + "vendor": "aws", + "instance_type": "nvidia-a10g", + "instance_size": "x1", + "framework": "pytorch", + "endpoint_type": "protected", + "namespace": None, + "image_url": None, + "env_vars": None, + }, + ), + ( + "examples/model_configs/endpoint_model_lite.yaml", + { + "model_name": "meta-llama/Llama-3.1-8B-Instruct", + # Defaults: + "revision": "main", + "model_dtype": None, + "endpoint_name": None, + "reuse_existing": False, + "accelerator": "gpu", + "region": "us-east-1", + "vendor": "aws", + "instance_type": None, + "instance_size": None, + "framework": "pytorch", + "endpoint_type": "protected", + "namespace": None, + "image_url": None, + "env_vars": None, + }, + ), + ( + "examples/model_configs/endpoint_model_reuse_existing.yaml", + {"endpoint_name": "llama-2-7B-lighteval", "reuse_existing": True}, + ), + ], + ) + def test_from_path(self, config_path, expected_config): + config = InferenceEndpointModelConfig.from_path(config_path) + for key, value in expected_config.items(): + assert getattr(config, key) == value From 54244b390dff1d12a5576a23e4ffb58d6c1fb182 Mon Sep 17 00:00:00 2001 From: Nick Doiron Date: Thu, 12 Dec 2024 05:45:35 -0500 Subject: [PATCH 09/12] Allow AdapterModels to have custom tokens (#306) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --------- Co-authored-by: Clémentine Fourrier <22726840+clefourrier@users.noreply.github.com> --- src/lighteval/main_accelerate.py | 4 ++-- src/lighteval/models/transformers/adapter_model.py | 12 ++++++++++++ 2 files changed, 14 insertions(+), 2 deletions(-) diff --git a/src/lighteval/main_accelerate.py b/src/lighteval/main_accelerate.py index 27e4141f5..3454a223b 100644 --- a/src/lighteval/main_accelerate.py +++ b/src/lighteval/main_accelerate.py @@ -169,11 +169,11 @@ def accelerate( # noqa C901 # Keeping only non null params args_dict = {k: v for k, v in args_dict.items() if v is not None} - if config["merged_weights"]["delta_weights"]: + if config["merged_weights"].get("delta_weights", False): if config["merged_weights"]["base_model"] is None: raise ValueError("You need to specify a base model when using delta weights") model_config = DeltaModelConfig(**args_dict) - elif config["merged_weights"]["adapter_weights"]: + elif config["merged_weights"].get("adapter_weights", False): if config["merged_weights"]["base_model"] is None: raise ValueError("You need to specify a base model when using adapter weights") model_config = AdapterModelConfig(**args_dict) diff --git a/src/lighteval/models/transformers/adapter_model.py b/src/lighteval/models/transformers/adapter_model.py index 449c2c1a8..e66a1aa1d 100644 --- a/src/lighteval/models/transformers/adapter_model.py +++ b/src/lighteval/models/transformers/adapter_model.py @@ -84,6 +84,18 @@ def _create_auto_model(self, config: AdapterModelConfig, env_config: EnvConfig) base = AutoModelForCausalLM.from_pretrained( config.base_model, torch_dtype=torch.float16, low_cpu_mem_usage=True, token=env_config.token ) + # resize model for adapters with added tokens + token_diff = len(self._tokenizer) - base.config.vocab_size + if token_diff != 0: + if token_diff > 0: + logger.info( + f"You're using the adapter model's tokenizer, which has more tokens than the base model. Adding {token_diff} token(s)." + ) + else: + logger.info( + f"You're using the adapter model's tokenizer, which has fewer tokens than the base model. Removing {abs(token_diff)} token(s)." + ) + base.resize_token_embeddings(len(self._tokenizer)) # Should pass revision model = PeftModel.from_pretrained(base, adapter_weights) model = model.merge_and_unload() From ca2fdcbb39531cbc9a4a029268b79d1b84a4452f Mon Sep 17 00:00:00 2001 From: Sadra Barikbin Date: Thu, 12 Dec 2024 14:57:21 +0330 Subject: [PATCH 10/12] Fix a tiny bug in `PromptManager::FewShotSampler::_init_fewshot_sampling_random` (#423) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --------- Co-authored-by: Clémentine Fourrier <22726840+clefourrier@users.noreply.github.com> --- src/lighteval/tasks/prompt_manager.py | 7 +-- tests/test_prompt_manager.py | 62 +++++++++++++++++++++++++++ 2 files changed, 66 insertions(+), 3 deletions(-) create mode 100644 tests/test_prompt_manager.py diff --git a/src/lighteval/tasks/prompt_manager.py b/src/lighteval/tasks/prompt_manager.py index 982a66549..cb9f94d04 100644 --- a/src/lighteval/tasks/prompt_manager.py +++ b/src/lighteval/tasks/prompt_manager.py @@ -92,7 +92,7 @@ def doc_to_fewshot_sorting_class(formatted_doc: Doc) -> str: formatted_doc (Doc): Formatted document. Returns: - str: Class of the + str: Class of the fewshot document """ return formatted_doc.fewshot_sorting_class or PromptManager.doc_to_target(formatted_doc) @@ -356,12 +356,13 @@ def _init_fewshot_sampling_sequential(self, num_fewshot: int, variance_seed: int self._fewshot_cache[variance_seed] = fewshotpool # Store few shot examples def _init_fewshot_sampling_random(self, variance_seed: int): - fewshotpool = self.task.fewshot_docs() + fewshotpool = list(self.task.fewshot_docs()) if variance_seed == 0: self._fewshot_cache[variance_seed] = fewshotpool else: # we shuffle rnd = random.Random(variance_seed) - self._fewshot_cache[variance_seed] = rnd.shuffle(fewshotpool) + rnd.shuffle(fewshotpool) + self._fewshot_cache[variance_seed] = fewshotpool def _init_fewshot_sampling_balanced( self, diff --git a/tests/test_prompt_manager.py b/tests/test_prompt_manager.py new file mode 100644 index 000000000..239f6fd6c --- /dev/null +++ b/tests/test_prompt_manager.py @@ -0,0 +1,62 @@ +# MIT License + +# Copyright (c) 2024 The HuggingFace Team + +# Permission is hereby granted, free of charge, to any person obtaining a copy +# of this software and associated documentation files (the "Software"), to deal +# in the Software without restriction, including without limitation the rights +# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +# copies of the Software, and to permit persons to whom the Software is +# furnished to do so, subject to the following conditions: + +# The above copyright notice and this permission notice shall be included in all +# copies or substantial portions of the Software. + +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +# SOFTWARE. + +import random +from collections import Counter + +import pytest + +from lighteval.tasks.lighteval_task import LightevalTask, LightevalTaskConfig +from lighteval.tasks.prompt_manager import FewShotSampler, PromptManager +from lighteval.tasks.requests import Doc + + +@pytest.mark.parametrize("fewshot_select", ["sequential", "random", "balanced"]) +def test_fewshot_sampler(fewshot_select: str): + config = LightevalTaskConfig( + name="test_fewshot_task", + prompt_function=lambda _, __: None, + hf_repo=None, + hf_subset="default", + metric=[], + few_shots_split="test", + few_shots_select=fewshot_select, + ) + task = LightevalTask("test_fewshot_task", config) + rnd = random.Random(0) + task._fewshot_docs = [ + Doc(str(i), ["A", "B"], rnd.randint(0, 2), fewshot_sorting_class=str(i % 20)) for i in range(100) + ] + sampler = FewShotSampler(task) + seed = 1 + docs = sampler.sample_fewshot_examples(20, seed) + match task.fewshot_selection: + case "balanced": + labels = Counter([PromptManager.doc_to_fewshot_sorting_class(d) for d in docs]) + assert labels.total() / len(labels) == 1 + case "sequential": + assert docs == task.fewshot_docs()[:20] + case "random": + rnd = random.Random(seed) + task_docs = task.fewshot_docs() + rnd.shuffle(task_docs) + assert docs == task_docs[:20] From 0135c2e6dc7ab273a8a5e2e33c3209541ddba8b7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Cl=C3=A9mentine=20Fourrier?= <22726840+clefourrier@users.noreply.github.com> Date: Thu, 12 Dec 2024 13:07:54 +0100 Subject: [PATCH 11/12] Fix custom arabic tasks (#440) * removed unused params * fix issue with task function --- community_tasks/_template.py | 2 -- community_tasks/arabic_evals.py | 18 +----------------- docs/source/adding-a-custom-task.mdx | 9 --------- docs/source/saving-and-reading-results.mdx | 2 -- examples/nanotron/custom_evaluation_tasks.py | 16 ---------------- examples/nanotron/custom_task.py | 4 ---- 6 files changed, 1 insertion(+), 50 deletions(-) diff --git a/community_tasks/_template.py b/community_tasks/_template.py index 345aebe4b..d0099ba26 100644 --- a/community_tasks/_template.py +++ b/community_tasks/_template.py @@ -99,8 +99,6 @@ def __init__( suite=["community"], generation_size=-1, stop_sequence=None, - output_regex=None, - frozen=False, ) diff --git a/community_tasks/arabic_evals.py b/community_tasks/arabic_evals.py index 382a780d3..07a096eca 100644 --- a/community_tasks/arabic_evals.py +++ b/community_tasks/arabic_evals.py @@ -109,8 +109,6 @@ def __init__( suite=["community"], generation_size=-1, stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -174,8 +172,6 @@ def __init__( suite=["community"], generation_size=-1, stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -241,8 +237,6 @@ def __init__( suite=["community"], generation_size=-1, stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -299,8 +293,6 @@ def __init__( suite=["community"], generation_size=-1, stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -361,8 +353,6 @@ def __init__( suite=["community"], generation_size=-1, stop_sequence=[], - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -423,9 +413,7 @@ def arabic_exams_pfn(line, task_name: str = None): def alghafa_pfn(line, task_name: str = None): question = line["query"] answer_index = int(line["label"]) - # Dynamically determining the choices by excluding '__few_shots', 'query' and 'label' - choices_keys = [key for key in line.keys() if key not in ["query", "label", "__few_shots"]] - choices = [line[key] for key in choices_keys] + choices = [line[key] for key in ["sol1", "sol2", "sol3", "sol4"]] instruction = "الأسئلة التالية هي أسئلة متعددة الإختيارات مع الجواب الصحيح\n\n" query = f"{instruction}السؤال: {question}\n" @@ -461,8 +449,6 @@ def __init__( suite=["community"], generation_size=-1, stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) @@ -839,8 +825,6 @@ def __init__( suite=["community"], generation_size=-1, stop_sequence=None, - output_regex=None, - frozen=False, trust_dataset=True, version=0, ) diff --git a/docs/source/adding-a-custom-task.mdx b/docs/source/adding-a-custom-task.mdx index 2fbff5524..e1823b7b9 100644 --- a/docs/source/adding-a-custom-task.mdx +++ b/docs/source/adding-a-custom-task.mdx @@ -107,8 +107,6 @@ class CustomSubsetTask(LightevalTaskConfig): suite=["community"], generation_size=-1, stop_sequence=None, - output_regex=None, - frozen=False, ) SUBSET_TASKS = [CustomSubsetTask(name=f"mytask:{subset}", hf_subset=subset) for subset in SAMPLE_SUBSETS] ``` @@ -154,13 +152,6 @@ Here is a list of the parameters and their meaning: for your generation - `metric` (list), the metrics you want to use for your evaluation (see next section for a detailed explanation) -- `output_regex` (str), A regex string that will be used to filter your - generation. (Generative metrics will only select tokens that are between the - first and the second sequence matched by the regex. For example, for a regex - matching `\n` and a generation `\nModel generation output\nSome other text` - the metric will only be fed with `Model generation output`) -- `frozen` (bool), for now, is set to False, but we will steadily pass all - stable tasks to True. - `trust_dataset` (bool), set to True if you trust the dataset. diff --git a/docs/source/saving-and-reading-results.mdx b/docs/source/saving-and-reading-results.mdx index 8c347cee2..993d7577b 100644 --- a/docs/source/saving-and-reading-results.mdx +++ b/docs/source/saving-and-reading-results.mdx @@ -170,9 +170,7 @@ The detail file contains the following columns: "stop_sequence": [ "Question=" ], - "output_regex": null, "num_samples": null, - "frozen": false, "suite": [ "lighteval" ], diff --git a/examples/nanotron/custom_evaluation_tasks.py b/examples/nanotron/custom_evaluation_tasks.py index 9ae066715..78c354916 100644 --- a/examples/nanotron/custom_evaluation_tasks.py +++ b/examples/nanotron/custom_evaluation_tasks.py @@ -267,8 +267,6 @@ def __init__( generation_size=40, trust_dataset=True, stop_sequence=None, - output_regex=None, - frozen=False, ): super().__init__( name=name, @@ -282,8 +280,6 @@ def __init__( few_shots_select=few_shots_select, suite=suite, generation_size=generation_size, - output_regex=output_regex, - frozen=frozen, trust_dataset=trust_dataset, stop_sequence=(stop_sequence if stop_sequence is not None else ["\n"]), ) @@ -370,8 +366,6 @@ def __init__( generation_size=-1, trust_dataset=True, stop_sequence=None, - output_regex=None, - frozen=False, ): super().__init__( name=name, @@ -387,8 +381,6 @@ def __init__( generation_size=generation_size, trust_dataset=trust_dataset, stop_sequence=(stop_sequence if stop_sequence is not None else ["\n"]), - output_regex=output_regex, - frozen=frozen, ) @@ -487,8 +479,6 @@ def __init__( generation_size=4, trust_dataset=True, stop_sequence=None, - output_regex=None, - frozen=False, ): super().__init__( name=name, @@ -504,8 +494,6 @@ def __init__( generation_size=generation_size, trust_dataset=trust_dataset, stop_sequence=(stop_sequence if stop_sequence is not None else ["\n"]), - output_regex=output_regex, - frozen=frozen, ) @@ -623,8 +611,6 @@ def __init__( generation_size=-1, trust_dataset=True, stop_sequence=None, - output_regex=None, - frozen=False, ): super().__init__( name=name, @@ -640,8 +626,6 @@ def __init__( generation_size=generation_size, trust_dataset=trust_dataset, stop_sequence=(stop_sequence if stop_sequence is not None else ["\n"]), - output_regex=output_regex, - frozen=frozen, ) diff --git a/examples/nanotron/custom_task.py b/examples/nanotron/custom_task.py index 05cea969f..feaa849ba 100644 --- a/examples/nanotron/custom_task.py +++ b/examples/nanotron/custom_task.py @@ -82,8 +82,6 @@ def mmlu_anatomy(line): generation_size=5, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, ), LightevalTaskConfig( name="mmlu:anatomy_signs", @@ -98,7 +96,5 @@ def mmlu_anatomy(line): generation_size=5, metric=[Metrics.loglikelihood_acc_single_token], stop_sequence=["\n"], - output_regex=None, - frozen=False, ), ] From 93a056fe4249281107baafc2a936e34f7f59e111 Mon Sep 17 00:00:00 2001 From: Albert Villanova del Moral <8515462+albertvillanova@users.noreply.github.com> Date: Thu, 12 Dec 2024 16:30:45 +0100 Subject: [PATCH 12/12] Fix imports from model_config (#443) --- docs/source/using-the-python-api.mdx | 2 +- src/lighteval/main_endpoint.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/using-the-python-api.mdx b/docs/source/using-the-python-api.mdx index 2e160a679..8c44050f4 100644 --- a/docs/source/using-the-python-api.mdx +++ b/docs/source/using-the-python-api.mdx @@ -11,7 +11,7 @@ After that, simply run the pipeline and save the results. ```python import lighteval from lighteval.logging.evaluation_tracker import EvaluationTracker -from lighteval.models.model_config import VLLMModelConfig +from lighteval.models.vllm.vllm_model import VLLMModelConfig from lighteval.pipeline import ParallelismManager, Pipeline, PipelineParameters from lighteval.utils.utils import EnvConfig from lighteval.utils.imports import is_accelerate_available diff --git a/src/lighteval/main_endpoint.py b/src/lighteval/main_endpoint.py index 952aae074..be75b711a 100644 --- a/src/lighteval/main_endpoint.py +++ b/src/lighteval/main_endpoint.py @@ -93,7 +93,7 @@ def openai( Evaluate OPENAI models. """ from lighteval.logging.evaluation_tracker import EvaluationTracker - from lighteval.models.model_config import OpenAIModelConfig + from lighteval.models.endpoints.openai_model import OpenAIModelConfig from lighteval.pipeline import EnvConfig, ParallelismManager, Pipeline, PipelineParameters env_config = EnvConfig(token=TOKEN, cache_dir=cache_dir) @@ -317,7 +317,7 @@ def tgi( import yaml from lighteval.logging.evaluation_tracker import EvaluationTracker - from lighteval.models.model_config import TGIModelConfig + from lighteval.models.endpoints.tgi_model import TGIModelConfig from lighteval.pipeline import EnvConfig, ParallelismManager, Pipeline, PipelineParameters env_config = EnvConfig(token=TOKEN, cache_dir=cache_dir)