168 lines
5.5 KiB
Python
168 lines
5.5 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import unittest
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from dotenv import dotenv_values
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env = dotenv_values('.env')
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from evalscope.run import run_task
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from evalscope.utils import is_module_installed, test_level_list
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from evalscope.utils.logger import get_logger
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logger = get_logger()
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class TestMTEB(unittest.TestCase):
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def setUp(self) -> None:
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self._check_env('mteb')
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def tearDown(self) -> None:
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pass
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@staticmethod
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def _check_env(module_name: str):
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if is_module_installed(module_name):
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logger.info(f'{module_name} is installed.')
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else:
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raise ModuleNotFoundError(f'run: pip install {module_name}')
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@unittest.skipUnless(0 in test_level_list(), 'skip test in current test level')
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def test_run_one_stage_mteb(self):
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task_cfg = {
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'eval_backend': 'RAGEval',
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'eval_config': {
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'tool': 'MTEB',
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'model': [
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{
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'model_name_or_path': 'AI-ModelScope/m3e-base',
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'pooling_mode': None, # load from model config
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'max_seq_length': 512,
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'prompt': '',
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'model_kwargs': {'torch_dtype': 'auto'},
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'encode_kwargs': {
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'batch_size': 128,
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},
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}
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],
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'eval': {
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'tasks': [
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# 'TNews',
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# 'CLSClusteringS2S',
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'T2Reranking',
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# 'T2Retrieval',
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# 'ATEC',
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],
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'verbosity': 2,
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'overwrite_results': True,
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'limits': 500,
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},
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},
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}
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run_task(task_cfg)
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@unittest.skipUnless(0 in test_level_list(), 'skip test in current test level')
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def test_run_one_stage_api(self):
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from evalscope import TaskConfig
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task_cfg = TaskConfig(
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eval_backend='RAGEval',
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eval_config={
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'tool': 'MTEB',
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'model': [
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{
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'model_name': 'text-embedding-v3',
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'api_base': 'https://dashscope.aliyuncs.com/compatible-mode/v1',
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'api_key': env.get('DASHSCOPE_API_KEY', 'EMPTY'),
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'dimensions': 1024,
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'encode_kwargs': {
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'batch_size': 10,
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},
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}
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],
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'eval': {
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'tasks': [
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'T2Retrieval',
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],
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'verbosity': 2,
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'overwrite_results': True,
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'limits': 30,
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},
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},
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)
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run_task(task_cfg)
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@unittest.skipUnless(0 in test_level_list(), 'skip test in current test level')
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def test_run_two_stage_mteb(self):
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task_cfg = {
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'eval_backend': 'RAGEval',
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'eval_config': {
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'tool': 'MTEB',
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'model': [
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{
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'model_name_or_path': 'AI-ModelScope/m3e-base',
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'is_cross_encoder': False,
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'max_seq_length': 512,
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'prompt': '',
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'model_kwargs': {'torch_dtype': 'auto'},
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'encode_kwargs': {
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'batch_size': 64,
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},
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},
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{
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'model_name_or_path': 'BAAI/bge-reranker-v2-m3',
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'is_cross_encoder': True,
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'max_seq_length': 512,
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'prompt': '为这个问题生成一个检索用的表示',
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'model_kwargs': {'torch_dtype': 'auto'},
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'encode_kwargs': {
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'batch_size': 32,
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},
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},
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],
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'eval': {
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'tasks': ['MedicalRetrieval', 'T2Retrieval'],
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'verbosity': 2,
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'overwrite_results': True,
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# 'limits': 10,
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'top_k': 10,
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},
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},
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}
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run_task(task_cfg)
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@unittest.skipUnless(0 in test_level_list(), 'skip test in current test level')
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def test_run_custom(self):
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task_cfg = {
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'eval_backend': 'RAGEval',
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'eval_config': {
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'tool': 'MTEB',
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'model': [
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{
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'model_name_or_path': 'AI-ModelScope/m3e-base',
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'pooling_mode': None, # load from model config
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'max_seq_length': 512,
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'prompt': '',
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'model_kwargs': {'torch_dtype': 'auto'},
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'encode_kwargs': {
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'batch_size': 128,
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},
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}
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],
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'eval': {
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'tasks': ['CustomRetrieval'],
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'dataset_path': 'custom_eval/text/retrieval',
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'verbosity': 2,
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'overwrite_results': True,
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'limits': 500,
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},
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},
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}
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run_task(task_cfg)
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if __name__ == '__main__':
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unittest.main(buffer=False)
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