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Gkumi
/
Distilled-bert-based

Token Classification
Transformers
Safetensors
distilbert
Model card Files Files and versions
xet
Community

Instructions to use Gkumi/Distilled-bert-based with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Gkumi/Distilled-bert-based with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("token-classification", model="Gkumi/Distilled-bert-based")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForTokenClassification
    
    tokenizer = AutoTokenizer.from_pretrained("Gkumi/Distilled-bert-based")
    model = AutoModelForTokenClassification.from_pretrained("Gkumi/Distilled-bert-based")
  • Notebooks
  • Google Colab
  • Kaggle
Distilled-bert-based
262 MB
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  • 1 contributor
History: 3 commits
Gkumi's picture
Gkumi
Upload tokenizer
4360c2d verified almost 2 years ago
  • .gitattributes
    1.52 kB
    initial commit almost 2 years ago
  • README.md
    5.17 kB
    Upload DistilBertForTokenClassification almost 2 years ago
  • config.json
    5.91 kB
    Upload DistilBertForTokenClassification almost 2 years ago
  • model.safetensors
    261 MB
    xet
    Upload DistilBertForTokenClassification almost 2 years ago
  • special_tokens_map.json
    125 Bytes
    Upload tokenizer almost 2 years ago
  • tokenizer.json
    669 kB
    Upload tokenizer almost 2 years ago
  • tokenizer_config.json
    1.2 kB
    Upload tokenizer almost 2 years ago
  • vocab.txt
    213 kB
    Upload tokenizer almost 2 years ago