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rg1683
/
fine_tuned_spiece_test_SentimentAnalysis

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

Instructions to use rg1683/fine_tuned_spiece_test_SentimentAnalysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use rg1683/fine_tuned_spiece_test_SentimentAnalysis with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="rg1683/fine_tuned_spiece_test_SentimentAnalysis")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("rg1683/fine_tuned_spiece_test_SentimentAnalysis")
    model = AutoModelForSequenceClassification.from_pretrained("rg1683/fine_tuned_spiece_test_SentimentAnalysis")
  • Notebooks
  • Google Colab
  • Kaggle
fine_tuned_spiece_test_SentimentAnalysis
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  • 1 contributor
History: 2 commits
rg1683's picture
rg1683
Upload BertForSequenceClassification
b76841a verified over 1 year ago
  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • README.md
    5.17 kB
    Upload BertForSequenceClassification over 1 year ago
  • config.json
    857 Bytes
    Upload BertForSequenceClassification over 1 year ago
  • model.safetensors
    404 MB
    xet
    Upload BertForSequenceClassification over 1 year ago