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Kantkamal
/
Gujarati-BERT-NER

Token Classification
Transformers
Safetensors
Gujarati
bert
gujarati
named-entity-recognition
ner
gujarati-bert
low-resource
Model card Files Files and versions
xet
Community

Instructions to use Kantkamal/Gujarati-BERT-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Kantkamal/Gujarati-BERT-NER with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("token-classification", model="Kantkamal/Gujarati-BERT-NER")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForTokenClassification
    
    tokenizer = AutoTokenizer.from_pretrained("Kantkamal/Gujarati-BERT-NER")
    model = AutoModelForTokenClassification.from_pretrained("Kantkamal/Gujarati-BERT-NER", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle

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  • .gitattributes
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  • README.md
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  • config.json
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  • model.safetensors
    948 MB
    xet
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  • special_tokens_map.json
    732 Bytes
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  • tokenizer.json
    6.41 MB
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  • tokenizer_config.json
    1.47 kB
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  • vocab.txt
    3.16 MB
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