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Upload H2Retrieval_IYun.py

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  1. H2Retrieval_IYun.py +60 -0
H2Retrieval_IYun.py ADDED
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+ # pip install pytrec-eval-terrier
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+ import pytrec_eval
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+ # conda install sentence-transformers -c conda-forge
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+ from sentence_transformers import SentenceTransformer
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+ import pandas as pd
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+ from collections import defaultdict
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+ import torch
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+ from tqdm import tqdm
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+
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+
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+ if torch.cuda.is_available():
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+ device = torch.device('cuda')
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+ else:
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+ device = torch.device('cpu')
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+
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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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+
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+ path = r'D:\datasets\H2Retrieval\data_sample5k'
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+ qrels_pd = load_dataset(path + r'\qrels.parquet.gz')
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+ corpus = load_dataset(path + r'\corpus.parquet.gz')
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+ queries = load_dataset(path + r'\queries.parquet.gz')
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+
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+ qrels = defaultdict(dict)
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+ for i, e in qrels_pd.iterrows():
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+ qrels[e['qid']][e['cid']] = e['score']
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+
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+ model = SentenceTransformer(r'D:\models\IYun', device='cuda:0')
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+
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+ corpusEmbeds = model.encode(corpus['text'].values, normalize_embeddings=True, show_progress_bar=True, batch_size=24)
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+ queriesEmbeds = model.encode(queries['text'].values, normalize_embeddings=True, show_progress_bar=True, batch_size=24)
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+
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+ queriesEmbeds = torch.tensor(queriesEmbeds, device=device)
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+ corpusEmbeds = corpusEmbeds.T
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+ corpusEmbeds = torch.tensor(corpusEmbeds, device=device)
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+
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+ def getTopK(corpusEmbeds, qEmbeds, k=10):
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+ scores = qEmbeds @ corpusEmbeds
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+ top_k_indices = torch.argsort(scores, descending=True)[:k]
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+ scores = scores.cpu()
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+ top_k_indices = top_k_indices.cpu()
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+ retn = {}
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+ for x in top_k_indices:
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+ x = int(x)
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+ retn[corpus['cid'][x]] = float(scores[x])
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+ return retn
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+
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+ results = {}
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+ for i in tqdm(range(len(queries)), desc="Converting"):
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+ results[queries['qid'][i]] = getTopK(corpusEmbeds, queriesEmbeds[i])
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+
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+ evaluator = pytrec_eval.RelevanceEvaluator(qrels, {'ndcg'})
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+ tmp = evaluator.evaluate(results)
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+ ndcg = 0
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+ for x in tmp.values():
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+ ndcg += x['ndcg']
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+ ndcg /= len(queries)
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+
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+ print(f'ndcg_10: {ndcg*100:.2f}%')