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Salahidine2002
/
result_model

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:80
loss:CoSENTLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use Salahidine2002/result_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Salahidine2002/result_model with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Salahidine2002/result_model")
    
    sentences = [
        "Woman in white in foreground and a man slightly behind walking with a sign for John's Pizza and Gyro in the background.",
        "They are working for John's Pizza.",
        "Two people walk away from a restaurant across a street.",
        "A couple are playing frisbee with a young child at the beach."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
result_model / eval
139 Bytes
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
Salahidine2002's picture
Salahidine2002
Training in progress, step 10
52a1142 verified 3 days ago
  • similarity_evaluation_pair-score-evaluator-dev_results.csv
    139 Bytes
    Training in progress, step 10 3 days ago