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Ezzaldin-97
/
STS-Arabert

Sentence Similarity
sentence-transformers
PyTorch
bert
feature-extraction
text-embeddings-inference
Model card Files Files and versions
xet
Community
1

Instructions to use Ezzaldin-97/STS-Arabert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Ezzaldin-97/STS-Arabert with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Ezzaldin-97/STS-Arabert")
    
    sentences = [
        "That is a happy person",
        "That is a happy dog",
        "That is a very happy person",
        "Today is a sunny day"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
STS-Arabert
544 MB
Ctrl+K
Ctrl+K
  • 2 contributors
History: 2 commits
Ezzaldin97
Semantic Textual Similarity Using Arabert
e3662d0 over 2 years ago
  • 1_Pooling
    Semantic Textual Similarity Using Arabert over 2 years ago
  • 2_Dense
    Semantic Textual Similarity Using Arabert over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    2.75 kB
    Semantic Textual Similarity Using Arabert over 2 years ago
  • added_tokens.json
    135 Bytes
    Semantic Textual Similarity Using Arabert over 2 years ago
  • config.json
    637 Bytes
    Semantic Textual Similarity Using Arabert over 2 years ago
  • config_sentence_transformers.json
    123 Bytes
    Semantic Textual Similarity Using Arabert over 2 years ago
  • modules.json
    341 Bytes
    Semantic Textual Similarity Using Arabert over 2 years ago
  • pytorch_model.bin

    Detected Pickle imports (3)

    • "torch.FloatStorage",
    • "torch._utils._rebuild_tensor_v2",
    • "collections.OrderedDict"

    What is a pickle import?

    541 MB
    xet
    Semantic Textual Similarity Using Arabert over 2 years ago
  • sentence_bert_config.json
    53 Bytes
    Semantic Textual Similarity Using Arabert over 2 years ago
  • special_tokens_map.json
    125 Bytes
    Semantic Textual Similarity Using Arabert over 2 years ago
  • tokenizer.json
    1.78 MB
    Semantic Textual Similarity Using Arabert over 2 years ago
  • tokenizer_config.json
    1.86 kB
    Semantic Textual Similarity Using Arabert over 2 years ago
  • vocab.txt
    761 kB
    Semantic Textual Similarity Using Arabert over 2 years ago