Upload H2Retrieval_bce.py

H2Retrieval_bce.py

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+# pip install pytrec-eval-terrier
+import pytrec_eval
+# 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
+
+
+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')
+
+qrels = defaultdict(dict)
+for i, e in qrels_pd.iterrows():
+    qrels[e['qid']][e['cid']] = e['score']
+
+model = SentenceTransformer(r'D:\models\bce', device='cuda:0')
+
+corpusEmbeds = model.encode(corpus['text'].values, normalize_embeddings=True, show_progress_bar=True, batch_size=24)
+queriesEmbeds = model.encode(queries['text'].values, normalize_embeddings=True, show_progress_bar=True, batch_size=24)
+
+queriesEmbeds = torch.tensor(queriesEmbeds, device=device)
+corpusEmbeds = corpusEmbeds.T
+corpusEmbeds = torch.tensor(corpusEmbeds, device=device)
+@torch.no_grad()
+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}%')