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Wonseong
/
FinBERT-FOMC-aspects

Text Classification
Transformers
Safetensors
English
bert
finance
fomc
sentiment-analysis
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use Wonseong/FinBERT-FOMC-aspects with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Wonseong/FinBERT-FOMC-aspects with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="Wonseong/FinBERT-FOMC-aspects")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("Wonseong/FinBERT-FOMC-aspects")
    model = AutoModelForSequenceClassification.from_pretrained("Wonseong/FinBERT-FOMC-aspects")
  • Notebooks
  • Google Colab
  • Kaggle
FinBERT-FOMC-aspects
440 MB
Ctrl+K
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  • 1 contributor
History: 10 commits
Wonseong's picture
Wonseong
Update README.md
6930866 verified about 1 month ago
  • .gitattributes
    1.52 kB
    initial commit about 2 months ago
  • README.md
    5.61 kB
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  • config.json
    938 Bytes
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  • mae_mse_curve.png
    52 kB
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  • metric_history.csv
    1.08 kB
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  • model.safetensors
    439 MB
    xet
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  • tokenizer.json
    712 kB
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  • tokenizer_config.json
    351 Bytes
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  • training_metadata.json
    1.43 kB
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  • zeroshot_fomc_sentiment_prompt.txt
    3.32 kB
    Update zeroshot_fomc_sentiment_prompt.txt about 2 months ago