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trtd56
/
practical_nlp_course_3

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

Instructions to use trtd56/practical_nlp_course_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use trtd56/practical_nlp_course_3 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="trtd56/practical_nlp_course_3")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("trtd56/practical_nlp_course_3")
    model = AutoModelForSequenceClassification.from_pretrained("trtd56/practical_nlp_course_3")
  • Notebooks
  • Google Colab
  • Kaggle
practical_nlp_course_3
275 MB
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  • 1 contributor
History: 3 commits
trtd56's picture
trtd56
Upload tokenizer
f0e32c0 verified over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    5.18 kB
    Upload DistilBertForSequenceClassification over 2 years ago
  • config.json
    781 Bytes
    Upload DistilBertForSequenceClassification over 2 years ago
  • model.safetensors
    275 MB
    xet
    Upload DistilBertForSequenceClassification over 2 years ago
  • special_tokens_map.json
    970 Bytes
    Upload tokenizer over 2 years ago
  • spiece.model
    439 kB
    xet
    Upload tokenizer over 2 years ago
  • tokenizer_config.json
    1.73 kB
    Upload tokenizer over 2 years ago