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procesaur
/
Emo355

Feature Extraction
sentence-transformers
Safetensors
Serbian
roberta
sentence-similarity
emotion-detection
serbian
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use procesaur/Emo355 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use procesaur/Emo355 with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("procesaur/Emo355")
    
    sentences = [
        "The weather is lovely today.",
        "It's so sunny outside!",
        "He drove to the stadium."
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [3, 3]
  • Notebooks
  • Google Colab
  • Kaggle
Emo355
1.42 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 9 commits
procesaur's picture
procesaur
Update README.md
488bfbd verified about 1 month ago
  • 1_Pooling
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  • 2_Dense
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  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • README.md
    117 kB
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  • config.json
    741 Bytes
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  • config_sentence_transformers.json
    296 Bytes
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  • labels_emo.json
    107 Bytes
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  • merges.txt
    498 kB
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  • model.safetensors
    1.42 GB
    xet
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  • modules.json
    360 Bytes
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  • sentence_bert_config.json
    250 Bytes
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  • sentence_xlnet_config.json
    241 Bytes
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  • special_tokens_map.json
    295 Bytes
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  • tokenizer.json
    3.58 MB
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  • tokenizer_config.json
    602 Bytes
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  • vocab.json
    832 kB
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