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toxigen.py
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toxigen.py
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import os
from eureka_ml_insights.core import (
DataProcessing,
EvalReporting,
Inference,
PromptProcessing,
)
from eureka_ml_insights.data_utils import (
AddColumn,
ColumnRename,
DataReader,
HFDataReader,
MMDataLoader,
SamplerTransform,
SequenceTransform,
)
from eureka_ml_insights.data_utils.toxigen_utils import (
GPT4ToxiGenRegex,
GPT4ToxiGenRegexGenerative,
)
from eureka_ml_insights.metrics import (
AverageAggregator,
CountAggregator,
ExactMatch,
)
from .config import (
AggregatorConfig,
DataProcessingConfig,
DataSetConfig,
EvalReportingConfig,
InferenceConfig,
MetricConfig,
PipelineConfig,
PromptProcessingConfig,
)
from .experiment_config import ExperimentConfig
from .model_configs import OAI_GPT4_1106_PREVIEW_CONFIG
"""This class specifies the config for running Toxigen discriminative benchmark."""
class ToxiGen_Discriminative_PIPELINE(ExperimentConfig):
def configure_pipeline(self, model_config):
# Configure the data pre processing component.
self.data_pre_processing = PromptProcessingConfig(
component_type=PromptProcessing,
data_reader_config=DataSetConfig(
HFDataReader,
{
"path": "toxigen/toxigen-data",
"tasks": ["annotated"],
# Note that "split" can be set to either ["test"] which will perform evaluation on 940 samples or
# ["train"] which will perform evaluation on 8,960 samples
"split": ["train"],
"transform": SequenceTransform(
[
ColumnRename(
name_mapping={
"text": "statement",
"target_group": "category",
"toxicity_human": "ground_truth",
}
),
]
),
},
),
prompt_template_path=os.path.join(
os.path.dirname(__file__), "../prompt_templates/toxigen_templates/discriminative_eval.jinja"
),
output_dir=os.path.join(self.log_dir, "data_pre_processing_output"),
)
# Inference component
self.inference_comp = InferenceConfig(
component_type=Inference,
model_config=model_config,
data_loader_config=DataSetConfig(
MMDataLoader,
{"path": os.path.join(self.data_pre_processing.output_dir, "transformed_data.jsonl")},
),
output_dir=os.path.join(self.log_dir, "inference_result"),
)
# Configure the data post processing component.
self.data_post_processing = DataProcessingConfig(
component_type=DataProcessing,
data_reader_config=DataSetConfig(
DataReader,
{
"path": os.path.join(self.inference_comp.output_dir, "inference_result.jsonl"),
"format": ".jsonl",
"transform": SequenceTransform(
[
AddColumn("merged_group"),
GPT4ToxiGenRegex("model_output", "ground_truth", "category", "merged_group"),
]
),
},
),
output_dir=os.path.join(self.log_dir, "data_post_processing_output"),
)
# Configure the evaluation and reporting component.
self.evalreporting_comp = EvalReportingConfig(
component_type=EvalReporting,
data_reader_config=DataSetConfig(
DataReader,
{
"path": os.path.join(self.data_post_processing.output_dir, "transformed_data.jsonl"),
"format": ".jsonl",
},
),
metric_config=MetricConfig(ExactMatch),
aggregator_configs=[
AggregatorConfig(CountAggregator, {"column_names": ["ExactMatch_result"], "normalize": True}),
AggregatorConfig(
CountAggregator, {"column_names": ["ExactMatch_result"], "group_by": "category", "normalize": True}
),
AggregatorConfig(
CountAggregator,
{"column_names": ["ExactMatch_result"], "group_by": "ground_truth", "normalize": True},
),
AggregatorConfig(
CountAggregator,
{"column_names": ["ExactMatch_result"], "group_by": "merged_group", "normalize": True},
),
],
output_dir=os.path.join(self.log_dir, "eval_report"),
)
return PipelineConfig(
[self.data_pre_processing, self.inference_comp, self.data_post_processing, self.evalreporting_comp],
self.log_dir,
)
"""This class specifies the config for running Toxigen generative benchmark."""
class ToxiGen_Generative_PIPELINE(ExperimentConfig):
def configure_pipeline(self, model_config):
# Data pre processing component.
self.data_pre_processing = PromptProcessingConfig(
component_type=PromptProcessing,
data_reader_config=DataSetConfig(
HFDataReader,
{
"path": "toxigen/toxigen-data",
"tasks": "prompts",
# Note that "split" can be set to one of the following 31 categories each one having 1000 samples:
#
"split": [
"hate_trans_1k",
"neutral_black_1k",
"hate_native_american_1k",
"neutral_immigrant_1k",
"hate_middle_east_1k",
"neutral_lgbtq_1k",
"neutral_women_1k",
"neutral_chinese_1k",
"hate_latino_1k",
"hate_bisexual_1k",
"hate_mexican_1k",
"hate_asian_1k",
"neutral_mental_disability_1k",
"neutral_mexican_1k",
"hate_mental_disability_1k",
"neutral_bisexual_1k",
"neutral_latino_1k",
"hate_chinese_1k",
"neutral_jewish_1k",
"hate_muslim_1k",
"neutral_asian_1k",
"hate_physical_disability_1k",
"hate_jewish_1k",
"neutral_muslim_1k",
"hate_immigrant_1k",
"hate_black_1k",
"hate_lgbtq_1k",
"hate_women_1k",
"neutral_middle_east_1k",
"neutral_native_american_1k",
"neutral_physical_disability_1k",
],
"transform": SequenceTransform(
[
SamplerTransform(sample_count=1550, random_seed=1234),
ColumnRename(
name_mapping={
"text": "prompt",
}
),
]
),
},
),
output_dir=os.path.join(self.log_dir, "data_pre_processing_output"),
)
# Inference component
self.inference_comp = InferenceConfig(
component_type=Inference,
model_config=model_config,
data_loader_config=DataSetConfig(
MMDataLoader,
{"path": os.path.join(self.data_pre_processing.output_dir, "transformed_data.jsonl")},
),
output_dir=os.path.join(self.log_dir, "inference_result"),
)
# Eval data pre processing component.
self.eval_data_pre_processing = PromptProcessingConfig(
component_type=PromptProcessing,
data_reader_config=DataSetConfig(
DataReader,
{
"path": os.path.join(self.inference_comp.output_dir, "inference_result.jsonl"),
"format": ".jsonl",
"transform": SequenceTransform([ColumnRename(name_mapping={"model_output": "statement"})]),
},
),
prompt_template_path=os.path.join(
os.path.dirname(__file__), "../prompt_templates/toxigen_templates/discriminative_eval.jinja"
),
output_dir=os.path.join(self.log_dir, "eval_data_pre_processing_output"),
)
# Eval Inference component
self.eval_inference_comp = InferenceConfig(
component_type=Inference,
model_config=OAI_GPT4_1106_PREVIEW_CONFIG,
data_loader_config=DataSetConfig(
MMDataLoader,
{"path": os.path.join(self.eval_data_pre_processing.output_dir, "transformed_data.jsonl")},
),
output_dir=os.path.join(self.log_dir, "eval_inference_result"),
)
# Eval data post processing component.
self.eval_data_post_processing = DataProcessingConfig(
component_type=DataProcessing,
data_reader_config=DataSetConfig(
DataReader,
{
"path": os.path.join(self.eval_inference_comp.output_dir, "inference_result.jsonl"),
"format": ".jsonl",
"transform": SequenceTransform(
[
AddColumn("ground_truth"),
AddColumn("category"),
AddColumn("merged_group"),
GPT4ToxiGenRegexGenerative(
"model_output",
"ground_truth",
"category",
"merged_group",
),
]
),
},
),
output_dir=os.path.join(self.log_dir, "eval_data_post_processing_output"),
)
# Configure the evaluation and reporting component.
self.evalreporting_comp = EvalReportingConfig(
component_type=EvalReporting,
data_reader_config=DataSetConfig(
DataReader,
{
"path": os.path.join(self.eval_data_post_processing.output_dir, "transformed_data.jsonl"),
"format": ".jsonl",
},
),
aggregator_configs=[
AggregatorConfig(AverageAggregator, {"column_names": ["model_output"], "ignore_non_numeric": True}),
AggregatorConfig(
AverageAggregator,
{"column_names": ["model_output"], "group_by": "ground_truth", "ignore_non_numeric": True},
),
AggregatorConfig(
AverageAggregator,
{"column_names": ["model_output"], "group_by": "category", "ignore_non_numeric": True},
),
AggregatorConfig(
AverageAggregator,
{"column_names": ["model_output"], "group_by": "merged_group", "ignore_non_numeric": True},
),
],
output_dir=os.path.join(self.log_dir, "eval_report"),
)
# Configure the pipeline
return PipelineConfig(
[
self.data_pre_processing,
self.inference_comp,
self.eval_data_pre_processing,
self.eval_inference_comp,
self.eval_data_post_processing,
self.evalreporting_comp,
],
self.log_dir,
)