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H2Retrieval_Dmeta_fix.py

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+# pip install pytrec-eval-terrier
+import pytrec_eval
+import json
+# conda install sentence-transformers -c conda-forge
+from sentence_transformers import SentenceTransformer
+import pandas as pd
+from collections import defaultdict
+import torch
+from tqdm import tqdm
+from tqdm.autonotebook import trange
+import random
+
+
+if torch.cuda.is_available():
+    device = torch.device('cuda')
+else:
+    device = torch.device('cpu')
+
+def load_dataset(path):
+    df = pd.read_parquet(path, engine="pyarrow")
+    return df
+
+path = r'D:\datasets\H2Retrieval\data_sample5k'
+qrels_pd = load_dataset(path + r'\qrels.parquet.gz')
+corpus = load_dataset(path + r'\corpus.parquet.gz')
+queries = load_dataset(path + r'\queries.parquet.gz')
+
+# sample_5k = sorted(random.sample(list(queries['qid'].values), k=5000))
+# queries = queries[queries['qid'].isin(sample_5k)]
+# qrels_pd = qrels_pd[qrels_pd['qid'].isin(sample_5k)]
+# corpus = corpus[corpus['cid'].isin(qrels_pd['cid'])]
+# corpus.to_parquet(
+#     r"D:\datasets\H2Retrieval\data_sample5k\corpus.parquet.gz",
+#     engine="pyarrow",
+#     compression="gzip",
+#     index=False
+# )
+# queries.to_parquet(
+#     r"D:\datasets\H2Retrieval\data_sample5k\queries.parquet.gz",
+#     engine="pyarrow",
+#     compression="gzip",
+#     index=False
+# )
+# qrels_pd.to_parquet(
+#     r"D:\datasets\H2Retrieval\data_sample5k\qrels.parquet.gz",
+#     engine="pyarrow",
+#     compression="gzip",
+#     index=False
+# )
+
+qrels = defaultdict(dict)
+for i, e in qrels_pd.iterrows():
+    qrels[e['qid']][e['cid']] = e['score']
+
+model = SentenceTransformer(r'D:\models\Dmeta', device='cuda:0')
+
+corpusEmbeds = model.encode(corpus['text'].values, normalize_embeddings=True, show_progress_bar=True, batch_size=32)
+queriesEmbeds = model.encode(queries['text'].values, normalize_embeddings=True, show_progress_bar=True, batch_size=32)
+
+queriesEmbeds = torch.tensor(queriesEmbeds, device=device)
+corpusEmbeds = corpusEmbeds.T
+corpusEmbeds = torch.tensor(corpusEmbeds, device=device)
+
+def getTopK(corpusEmbeds, qEmbeds, k=10):
+    scores = qEmbeds @ corpusEmbeds
+    top_k_indices = torch.argsort(scores, descending=True)[:k]
+    scores = scores.cpu()
+    top_k_indices = top_k_indices.cpu()
+    retn = {}
+    for x in top_k_indices:
+        x = int(x)
+        retn[corpus['cid'][x]] = float(scores[x])
+    return retn
+
+results = {}
+for i in tqdm(range(len(queries)), desc="Converting"):
+    results[queries['qid'][i]] = getTopK(corpusEmbeds, queriesEmbeds[i])
+
+evaluator = pytrec_eval.RelevanceEvaluator(qrels, {'ndcg'})
+tmp = evaluator.evaluate(results)
+ndcg = 0
+for x in tmp.values():
+    ndcg += x['ndcg']
+ndcg /= len(queries)
+
+print(f'ndcg_10: {ndcg*100:.2f}%')

data_sample5k/corpus.parquet.gz

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+oid sha256:64b4e28cc115b2d25700b67568e4d8bf17ebdc97f443e08637f1b493502de340
+size 2384921

data_sample5k/qrels.parquet.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:33df50dea5812644cebea79d9d5e714f2c7d331b726c835444e421c952c766ca
+size 115556

data_sample5k/queries.parquet.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:afcab206259b5e918a8889f6b4f0e8eb3c4ffbad139d7b6b147695fe25349817
+size 643048