Upload H2Retrieval_Dmeta.py
Browse files- H2Retrieval_Dmeta.py +76 -0
H2Retrieval_Dmeta.py
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from sentence_transformers import SentenceTransformer
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from mteb import MTEB
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from mteb.abstasks.AbsTaskRetrieval import AbsTaskRetrieval
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from datasets import DatasetDict
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from collections import defaultdict
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import pandas as pd
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def load_dataset(path):
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df = pd.read_parquet(path, engine="pyarrow")
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return df
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def load_retrieval_data(path):
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eval_split = 'dev'
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corpus = {e['cid']: {'text': e['text']} for i, e in load_dataset(path + r'\data\corpus.parquet.gz').iterrows()}
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queries = {e['qid']: e['text'] for i, e in load_dataset(path + r'\data\queries.parquet.gz').iterrows()}
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relevant_docs = defaultdict(dict)
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for i, e in load_dataset(path + r'\data\qrels.parquet.gz').iterrows():
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relevant_docs[e['qid']][e['cid']] = e['score']
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corpus = DatasetDict({eval_split: corpus})
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queries = DatasetDict({eval_split: queries})
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relevant_docs = DatasetDict({eval_split: relevant_docs})
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return corpus, queries, relevant_docs
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# conda install sentence-transformers -c conda-forge
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# $env:HF_ENDPOINT="https://hf-mirror.com"; python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='DMetaSoul/Dmeta-embedding', local_dir=r'D:\models\Dmeta')"
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# pip install pytrec-eval-terrier
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# 修改 envs\HelloGPT\lib\site-packages\pip\_vendor\resolvelib\resolvers.py 的 Resolution 对象
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# 的 _get_updated_criteria 方法,给 for 循环里添加如下代码
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# def _get_updated_criteria(self, candidate):
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# criteria = self.state.criteria.copy()
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# for requirement in self._p.get_dependencies(candidate=candidate):
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# if 'pytrec-eval' in repr(requirement):
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# continue
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# self._add_to_criteria(criteria, requirement, parent=candidate)
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# return criteria
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# pip install mteb[beir] -i https://pypi.tuna.tsinghua.edu.cn/simple/ # 需要开 tun 模式
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# mteb 会给 encode 的 batch_size 设置 128, 显存不够得手动修改 SentenceTransformer.py
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# def encode 的相关内容, 将 batch_size 强制调回 32, 添加一行 batch_size = 32
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model = SentenceTransformer(r'D:\models\Dmeta', device='cuda:0')
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texts1 = ["胡子长得太快怎么办?", "在香港哪里买手表好"]
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texts2 = ["胡子长得快怎么办?", "怎样使胡子不浓密!", "香港买手表哪里好", "在杭州手机到哪里买"]
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embs1 = model.encode(texts1, normalize_embeddings=True)
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embs2 = model.encode(texts2, normalize_embeddings=True)
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similarity = embs1 @ embs2.T
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print(similarity)
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class H2Retrieval(AbsTaskRetrieval):
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@property
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def description(self):
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return {
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'name': 'H2Retrieval',
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'hf_hub_name': 'Limour/H2Retrieval',
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'reference': 'https://huggingface.co/datasets/a686d380/h-corpus-2023',
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'description': 'h-corpus 领域的 Retrieval 评价数据集。',
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'type': 'Retrieval',
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'category': 's2p',
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'eval_splits': ['dev'],
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'eval_langs': ['zh'],
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'main_score': 'ndcg_at_10'
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}
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def load_data(self, **kwargs):
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if self.data_loaded:
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return
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self.corpus, self.queries, self.relevant_docs = load_retrieval_data(r'D:\datasets\H2Retrieval')
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self.data_loaded = True
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evaluation = MTEB(tasks=[H2Retrieval()])
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evaluation.run(model)
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# torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 25.34 GiB.
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# return torch.mm(a_norm, b_norm.transpose(0, 1)) #TODO: this keeps allocating GPU memory
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# 无语了,最耗时间的跑完了,这里给我整不会了
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