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CTB2001
/
PatentSBERTa-green-classifier

Text Classification
Transformers
Safetensors
sentence-transformers
English
mpnet
patent-classification
green-technology
fine-tuned
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use CTB2001/PatentSBERTa-green-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use CTB2001/PatentSBERTa-green-classifier with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="CTB2001/PatentSBERTa-green-classifier")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("CTB2001/PatentSBERTa-green-classifier")
    model = AutoModelForSequenceClassification.from_pretrained("CTB2001/PatentSBERTa-green-classifier")
  • sentence-transformers

    How to use CTB2001/PatentSBERTa-green-classifier with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("CTB2001/PatentSBERTa-green-classifier")
    
    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
PatentSBERTa-green-classifier
439 MB
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  • 1 contributor
History: 3 commits
CTB2001's picture
CTB2001
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4d1139b verified 2 months ago
  • .gitattributes
    1.52 kB
    initial commit 2 months ago
  • README.md
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  • config.json
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  • model.safetensors
    438 MB
    xet
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  • tokenizer.json
    712 kB
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  • tokenizer_config.json
    365 Bytes
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