Merge branch 'main' into Document-Custom-Model-Files

community_tasks/arabic_evals.py

@@ -28,8 +28,11 @@
 """
 import random
 import re
+from typing import Any, Dict, List, Optional, Union
 
-from lighteval.metrics.metrics import Metrics
+from lighteval.metrics.llm_as_judge import JudgeLM
+from lighteval.metrics.metrics import Metric, MetricCategory, Metrics
+from lighteval.metrics.utils.metric_utils import MetricUseCase
 from lighteval.tasks.default_prompts import LETTER_INDICES
 from lighteval.tasks.lighteval_task import LightevalTaskConfig
 from lighteval.tasks.requests import Doc
@@ -832,6 +835,215 @@ def __init__(
 ]
 
 
+class JudgeMetricWrapper(Metric):
+    """Wrapper class for LLM-based judge metric implementation."""
+
+    def __init__(self, judge: JudgeLM):
+        """
+        Initializes the judge metric wrapper.
+
+        Args:
+            judge (JudgeLM): The LLM judge instance to use for evaluation.
+        """
+        self.judge = judge
+        self.metric_name = "llm_as_judge"
+        self.category = MetricCategory.LLM_AS_JUDGE
+        self.corpus_level_fn = self.aggregate_scores
+        self.sample_level_fn = self._sample_level_fn
+        self.higher_is_better = True  # Fixed tuple syntax
+        self.use_case = MetricUseCase.NONE
+
+    def compute(self, responses: list[str], formatted_docs: list[Doc], **kwargs) -> dict[str, float]:
+        """
+        Computes evaluation scores using the judge's evaluate_answer method.
+
+        Args:
+            responses (list[str]): The predicted answers
+            formatted_docs (list[Doc]): Documents containing questions and gold answers
+
+        Returns:
+            dict[str, float]: Dictionary containing evaluation scores
+        """
+        results = []
+        for i, doc in enumerate(formatted_docs):
+            question = doc.query
+            gold = doc.choices[doc.gold_index] if doc.gold_index is not None else None
+            answer = responses[i][0].result[0]
+
+            score, _, _ = self.judge.evaluate_answer(question=question, answer=answer, options=None, gold=gold)
+            results.append({self.metric_name: score})
+
+        return results
+
+    def aggregate_scores(self, scores: list[dict]) -> float:
+        return sum(scores) / len(scores) if scores else 0.0
+
+    def _sample_level_fn(self):
+        return None
+
+
+def parse_candidates(candidates: Union[List[str], str]) -> List[str]:
+    """
+    Parses and validates candidate answers from either list or string format.
+
+    Args:
+        candidates: Either a list of candidate answers or a newline-separated string
+
+    Returns:
+        List[str]: List of validated candidate answers
+
+    Raises:
+        ValueError: If candidates cannot be parsed or are empty
+    """
+    try:
+        if isinstance(candidates, list):
+            parsed_candidates = [str(c).strip() for c in candidates if c]
+        else:
+            parsed_candidates = [c.strip() for c in str(candidates).split("\n") if c.strip()]
+
+        if not parsed_candidates:
+            raise ValueError("No valid candidates found after parsing")
+
+        return parsed_candidates
+    except Exception as e:
+        raise ValueError(f"Failed to parse candidates: {str(e)}")
+
+
+def qa_prompt_arabic(line: Dict[str, Any], task_name: str = None) -> Doc:
+    """
+    Formats the prompt for Arabic question answering with candidates.
+
+    Args:
+        line: Dictionary containing question and candidate information
+        task_name: Optional name for the task
+
+    Returns:
+        Doc: Formatted document for evaluation
+
+    Raises:
+        ValueError: If required fields are missing or invalid
+    """
+    try:
+        # Validates and extracts the question
+        if not isinstance(line.get("question"), str):
+            raise ValueError("Question must be a string")
+        question = line["question"]
+
+        # Processes candidate answers
+        candidates = parse_candidates(line["candidates"])
+
+        # Validates gold answer
+        if "gold_answer" not in line:
+            raise ValueError("Gold answer is required")
+        gold_answer = str(line["gold_answer"])
+
+        # Constructs the prompt
+        instruction = "بناءً على السياقات المقترحة التالية، اجب عن السؤال التالي"
+        query = f"{instruction}\n\nالسؤال:\n{question}\n\nالسياقات المقترحة:\n{', '.join(candidates)}\n"
+
+        return Doc(
+            task_name=task_name or "alrage",
+            query=query,
+            instruction=instruction,
+            choices=[gold_answer],  # Gold answer is used as the only valid choice
+            gold_index=0,  # Index of the correct answer in choices
+        )
+    except Exception as e:
+        raise ValueError(f"Failed to create QA prompt: {str(e)}")
+
+
+def judge_template(question: str, answer: str, gold: str, options: Optional[List[str]] = None) -> List[Dict[str, str]]:
+    """
+    Template for the Arabic judge prompt.
+
+    System prompt translation:
+    You are a neutral expert evaluator. Your tasks are:
+    1. Evaluate the answer's accuracy compared to the correct answer
+    2. Verify that the answer is supported by the provided context
+    3. Evaluate the quality and comprehensiveness of the answer
+    Rate the answer on a scale from 0 to 10.
+
+    Args:
+        question: The question being evaluated
+        answer: The provided answer
+        gold: The correct answer
+        options: Optional list of answer choices
+
+    Returns:
+        List[Dict[str, str]]: Formatted messages for the judge
+    """
+    messages = [
+        {
+            "role": "system",
+            "content": """أنت مقيّم محايد خبير باللغة العربية. يجب عليك:
+1. تقييم دقة الإجابة مقارنة بالإجابة الصحيحة
+2. التحقق من أن الإجابة مدعومة بالسياق المقدم
+3. تقييم جودة وشمولية الإجابة
+
+مهم جداً: يجب أن يكون ردك رقماً فقط من 0 إلى 10. لا تضف أي نص أو تفسير.""",
+        },
+        {
+            "role": "user",
+            "content": f"""السؤال: {question}
+
+الإجابة المقدمة: {answer}
+
+الإجابة الصحيحة: {gold}
+
+أعط تقييماً من 0 إلى 10:
+0-2: إجابة خاطئة تماماً
+3-4: إجابة جزئية مع أخطاء
+5-6: إجابة متوسطة
+7-8: إجابة جيدة
+9-10: إجابة ممتازة
+
+اكتب رقماً فقط من 0 إلى 10 بدون أي نص إضافي:""",
+        },
+    ]
+    return messages
+
+
+def process_judge_response(response) -> float:
+    """Process the judge's response to extract the score"""
+    # If response is a list, extract the content from the user role
+    if isinstance(response, list):
+        response_content = " ".join(item["content"] for item in response if item["role"] == "user")
+    else:
+        response_content = response  # If it's not a list, use it directly
+
+    try:
+        # Extract the score from the response content
+        score = float(next(num for num in response_content.split() if num.replace(".", "", 1).isdigit()))
+        return min(max(score / 10.0, 0.0), 1.0)
+    except (StopIteration, ValueError):
+        return 0.0
+
+
+judge = JudgeLM(
+    model="Qwen/Qwen2.5-72B-Instruct",
+    templates=judge_template,
+    process_judge_response=process_judge_response,
+    judge_backend="vllm",
+)
+
+wrapped_judge = JudgeMetricWrapper(judge)
+
+# Task configuration
+alrage_qa_task = LightevalTaskConfig(
+    name="alrage_qa",
+    prompt_function=qa_prompt_arabic,
+    suite=["community"],
+    hf_repo="OALL/ALRAGE",
+    hf_subset=None,
+    hf_avail_splits=["train"],
+    evaluation_splits=["train"],
+    metric=[wrapped_judge],
+    trust_dataset=True,
+    generation_size=200,
+    stop_sequence=[],
+    version=0,
+)
+
 TASKS_TABLE = (
     ARABIC_MMLU_TASKS
     + ARABIC_MMLU_HT_TASKS
@@ -852,4 +1064,5 @@ def __init__(
     + [hellaswag_okapi_ar_task]
     + [toxigen_ar_task]
     + [sciq_ar_task]
+    + [alrage_qa_task]
 )

examples/tasks/OALL_v2_tasks.txt

@@ -115,3 +115,4 @@ community|arabic_mmlu_ht:sociology|0|0
 community|arabic_mmlu_ht:us_foreign_policy|0|0
 community|arabic_mmlu_ht:virology|0|0
 community|arabic_mmlu_ht:world_religions|0|0
+community|alrage_qa|0|0

pyproject.toml

@@ -57,7 +57,7 @@ dependencies = [
     "transformers>=4.38.0",
     "accelerate",
     "huggingface_hub>=0.23.0",
-    "torch>=2.0,<2.5",
+    "torch>=2.0,<3.0",
     "GitPython>=3.1.41", # for logging
     "datasets>=2.14.0",
     "numpy<2",  # pinned to avoid incompatibilities

src/lighteval/tasks/default_prompts.py

@@ -120,7 +120,7 @@ def asdiv(line, task_name: str = None):
 
 def babi_qa(line, task_name: str = None):  # HELM
     def process_path(path: str) -> str:
-        """Turn a path string (task 19) from the original format 's,w' to a verbal model-friendly format 'south west'"""
+        """Turn a path string (task 19) from the original format 's,w' into a verbal model-friendly format 'south west'"""
         steps = path.split(",")
         directions = {"s": "south", "n": "north", "e": "east", "w": "west"}
         path = " ".join([directions[step] for step in steps])
@@ -281,7 +281,7 @@ def bbh_logical_deduction_three_objects(line, task_name: str = None):
 def bbh_movie_recommendation(line, task_name: str = None):
     if line["target"] == "Monsters, Inc":  # this line is not correctly formatted
         logger.warning(
-            "One sample removed from task bbh:movie_recommentation because its line is incorrectly formatted."
+            "One sample removed from task bbh:movie_recommendation because its line is incorrectly formatted."
         )
         return []
     instruction = "Recommend movies similar to the given list of movies.\n\n"
@@ -500,7 +500,7 @@ def civil_comments(line, task_name: str = None):
 def cnn_dm(line, task_name: str = None):
     return Doc(
         task_name=task_name,
-        query=f"###\nArticle:{line['article']}\n\nSummarize the above article in 3 sentence.\n",
+        query=f"###\nArticle:{line['article']}\n\nSummarize the above article in 3 sentences.\n",
         choices=[str(line["summary"])],
         gold_index=0,
         specific={"text": line["article"]},
@@ -730,7 +730,7 @@ def gpqa(line, task_name: str = None):
 
 
 def gsm8k(line, task_name: str = None):
-    # Has special analysis in metric for number decomposiition
+    # Has special analysis in metric for number decomposition
     return Doc(
         task_name=task_name,
         query=f"Question: {line['question']}\nAnswer:",
@@ -2076,7 +2076,7 @@ def rte(line, task_name: str = None):
     return Doc(
         task_name=task_name,
         query=f"{line['sentence1']}\nQuestion: {line['sentence2']} True or False?\nAnswer:",
-        choices=[" True", " False"],  # 0 = entailement, 1 = not entailment
+        choices=[" True", " False"],  # 0 = entailment, 1 = not entailment
         gold_index=int(line["label"]),
         # "metric": "choices_loglikelihood",
     )

src/lighteval/tasks/lighteval_task.py

@@ -621,7 +621,7 @@ def create_requests_from_tasks(  # noqa: C901
         n_samples = min(max_samples, len(task_docs)) if max_samples else len(task_docs)
         evaluation_tracker.task_config_logger.log_num_docs(task_name, len(task_docs), n_samples)
 
-        # logs out the diferent versions of the tasks for every few shot
+        # logs out the different versions of the tasks for every few shot
         for num_fewshot, _ in fewshot_dict[task_name]:
             cur_task_name = f"{task_name}|{num_fewshot}"
             evaluation_tracker.versions_logger.log(cur_task_name, task.version)
@@ -633,7 +633,7 @@ def create_requests_from_tasks(  # noqa: C901
         prompt_manager = PromptManager(lm=lm, task=task)
         seeds = prompt_manager.few_shot_sampler.get_fewshot_seeds(num_fewshot_seeds)
 
-        # We can do several round of fewshots sampling to get some variance informations
+        # We can do several round of fewshots sampling to get some variance information
         for seed in seeds:
             for doc_id in range(n_samples):
                 doc_id_seed = f"{doc_id}_{seed}"  # if we do several rounds of few shot sampling we have several seeds

src/lighteval/tasks/prompt_manager.py

@@ -132,7 +132,7 @@ def _multi_turn_contexts(self, doc: Doc, use_chat_template: bool, system_prompt:
         Multi turn tasks need use chat templating.
 
         Args:
-            doc (Doc): Formated document.
+            doc (Doc): Formatted document.
             use_chat_template (bool): wether or not to use chat template. Will fail if false.
             system_prompt (Optional[str]): The system prompt to use
             tokenizer (PreTrainedTokenizer): The tokenizer used for the chat template

src/lighteval/tasks/registry.py

@@ -166,7 +166,7 @@ def _task_superset_dict(self):
                 "lighteval|mmlu" -> ["lighteval|mmlu:abstract_algebra", "lighteval|mmlu:college_biology", ...]
             }
         """
-        # Note: sorted before groupby is imporant as the python implementation of groupby does not
+        # Note: sorted before groupby is important as the python implementation of groupby does not
         # behave like sql groupby. For more info see the docs of itertools.groupby
         superset_dict = {k: list(v) for k, v in groupby(sorted(self.task_registry.keys()), lambda x: x.split(":")[0])}
         # Only consider supersets with more than one task

src/lighteval/tasks/templates/continuation.py

@@ -112,7 +112,7 @@ def get_continuation_prompt_function(
     C. Continuation 3
     Answer: A/B/C
 
-    This template is very similar to the `Multiple Choice` template, except that it only takes context/continuations as input and don't use the anchor labels (Question/Answer)
+    This template is very similar to the `Multiple Choice` template, except that it only takes context/continuations as input and doesn't use the anchor labels (Question/Answer)
 
     Args:
         language (Language): The language of the Continuation task.

src/lighteval/tasks/templates/copa.py

@@ -86,17 +86,17 @@ def get_copa_prompt_function(
 
     Format:
     *CF*
-    Context Premise thefore/cause | (Continuation 1, Continuation 2, Continuation 3)
+    Context Premise therefore/cause | (Continuation 1, Continuation 2, Continuation 3)
 
     *Hybrid*
-    Context Premise thefore/cause
+    Context Premise therefore/cause
     A. Continuation 1
     B. Continuation 2
     C. Continuation 3
     Answer: | Continuation 1/Continuation 2/Continuation 3
 
     *MCF*
-    Context Premise thefore/cause
+    Context Premise therefore/cause
     A. Continuation 1
     B. Continuation 2
     C. Continuation 3

src/lighteval/tasks/templates/hellaswag.py

@@ -70,7 +70,7 @@ def get_hellaswag_prompt_function(
     Create a templated prompt function for a Hellaswag task.
 
     Format:
-    Context Premise thefore/cause | (Continuation 1, Continuation 2, Continuation 3)
+    Context Premise therefore/cause | (Continuation 1, Continuation 2, Continuation 3)
 
     Args:
         language (Language): The language of the Hellaswag task.
@@ -126,7 +126,7 @@ def hellaswag_prompt(
         if ctx_b:
             ctx_a = join_ctxs(ctx_a, ctx_b)
 
-        # Removoal of the [header] can happen and we need the first letter to be capital afterwards
+        # Removal of the [header] can happen and we need the first letter to be capital afterwards
         full_context = HELLASWAG_QUERY.format(activity_label=activity_label, ctx=ctx_a)
         choices = [
             hellaswag_preprocess(