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

  • Libraries
  • sentence-transformers

    How to use mkiram/result_model with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("mkiram/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.",
        "A woman ordering pizza.",
        "The people are eating omelettes.",
        "Some people board a train."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
result_model / eval
141 Bytes
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
mkiram's picture
mkiram
Training in progress, step 10
eb4b0fc verified 3 months ago
  • similarity_evaluation_pair-score-evaluator-dev_results.csv
    141 Bytes
    Training in progress, step 10 3 months ago