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full evals / full scoring flow

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Xi Yan 2024-10-15 10:17:45 -07:00
parent cccd5be090
commit be4f395032
4 changed files with 88 additions and 109 deletions
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82
llama_stack/apis/evals/client.py
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@ -119,52 +119,48 @@ async def run_main(host: str, port: int, eval_dataset_path: str = ""):
cprint(f"{k}: {v}", "green")
# Scoring Task
# # 1. register huggingface dataset
# response = await dataset_client.create_dataset(
# dataset_def=HuggingfaceDatasetDef(
# identifier="Llama-3.1-8B-Instruct-evals__mmlu_pro__details",
# dataset_path="meta-llama/Llama-3.1-8B-Instruct-evals",
# dataset_name="Llama-3.1-8B-Instruct-evals__mmlu_pro__details",
# rename_columns_map={
# "output_parsed_answer": "generated_answer",
# "input_correct_responses": "expected_answer",
# },
# kwargs={"split": "latest"},
# )
# )
# cprint(response, "cyan")
# 1. register huggingface dataset
response = await dataset_client.create_dataset(
dataset_def=HuggingfaceDatasetDef(
identifier="Llama-3.1-8B-Instruct-evals__mmlu_pro__details",
dataset_path="meta-llama/Llama-3.1-8B-Instruct-evals",
dataset_name="Llama-3.1-8B-Instruct-evals__mmlu_pro__details",
rename_columns_map={
"output_parsed_answer": "generated_answer",
"input_correct_responses": "expected_answer",
},
kwargs={"split": "latest"},
)
)
cprint(response, "cyan")
# # register custom dataset from file path
# response = await dataset_client.create_dataset(
# dataset_def=CustomDatasetDef(
# identifier="rag-evals",
# url=data_url_from_file(eval_dataset_path),
# rename_columns_map={
# "query": "input_query",
# },
# )
# )
# cprint(response, "cyan")
# register custom dataset from file path
response = await dataset_client.create_dataset(
dataset_def=CustomDatasetDef(
identifier="rag-evals",
url=data_url_from_file(eval_dataset_path),
)
)
cprint(response, "cyan")
# # 2. run evals on the registered dataset
# response = await client.run_scorer(
# dataset_config=EvaluateDatasetConfig(
# dataset_identifier="rag-evals",
# # dataset_identifier="Llama-3.1-8B-Instruct-evals__mmlu_pro__details",
# row_limit=10,
# ),
# eval_scoring_config=EvaluateScoringConfig(
# scorer_config_list=[
# EvaluateSingleScorerConfig(scorer_name="accuracy"),
# EvaluateSingleScorerConfig(
# scorer_name="braintrust::answer-correctness"
# ),
# ]
# ),
# )
# 2. run evals on the registered dataset
response = await client.run_scorer(
dataset_config=EvaluateDatasetConfig(
dataset_identifier="rag-evals",
row_limit=10,
),
eval_scoring_config=EvaluateScoringConfig(
scorer_config_list=[
EvaluateSingleScorerConfig(scorer_name="accuracy"),
EvaluateSingleScorerConfig(
scorer_name="braintrust::answer-correctness"
),
]
),
)
# for k, v in response.eval_result.metrics.items():
# cprint(f"{k}: {v}", "green")
for k, v in response.eval_result.metrics.items():
cprint(f"{k}: {v}", "green")
def main(host: str, port: int, eval_dataset_path: str = ""):