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Bharatdeep-H
/
pq_cache

Sentence Similarity
sentence-transformers
Safetensors
mpnet
feature-extraction
Generated from Trainer
dataset_size:3320
loss:MultipleNegativesRankingLoss
loss:CosineSimilarityLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use Bharatdeep-H/pq_cache with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Bharatdeep-H/pq_cache with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Bharatdeep-H/pq_cache")
    
    sentences = [
        "How did my portfolio perform during the last 18 days?",
        "What is the performance of my portfolio over the last 18 days?",
        "Show me the geographic distribution of my investments\n",
        "Show me recommendations on improving returns and risk"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
pq_cache / eval
2.71 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 1 commit
Bharatdeep-H's picture
Bharatdeep-H
Upload query normalization model
6255bae verified about 1 year ago
  • Information-Retrieval_evaluation_test-eval_results.csv
    2.71 kB
    Upload query normalization model about 1 year ago