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logasanjeev
/
bert-emotion-classifier

Text Classification
Transformers
ONNX
Safetensors
PyTorch
English
bert
multi-label-classification
multi-class-classification
emotion
go_emotions
emotion-classification
sentiment-analysis
tensorflow
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use logasanjeev/bert-emotion-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use logasanjeev/bert-emotion-classifier with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="logasanjeev/bert-emotion-classifier")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("logasanjeev/bert-emotion-classifier")
    model = AutoModelForSequenceClassification.from_pretrained("logasanjeev/bert-emotion-classifier", device_map="auto")
  • Inference
  • Notebooks
  • Google Colab
  • Kaggle
bert-emotion-classifier
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  • 1 contributor
History: 90 commits
logasanjeev's picture
logasanjeev
Update README.md
4dc3894 verified 3 months ago
  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • LICENSE
    1.08 kB
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  • README.md
    15.8 kB
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  • class_wise_f1_plot.png
    46 kB
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  • class_wise_metrics.csv
    1.5 kB
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  • config.json
    2.14 kB
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  • emotion_labels.json
    333 Bytes
    Upload emotion_labels.json for updated GoEmotions BERT model over 1 year ago
  • inference.py
    3.79 kB
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  • model.onnx
    438 MB
    xet
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  • model.safetensors
    438 MB
    xet
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  • onnx_inference.py
    3.8 kB
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  • optimized_thresholds.json
    947 Bytes
    Update optimized_thresholds.json to dictionary format with emotion_labels and thresholds over 1 year ago
  • special_tokens_map.json
    125 Bytes
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  • tokenizer.json
    711 kB
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  • tokenizer_config.json
    1.27 kB
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  • training_curves_plot.png
    55.3 kB
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  • vocab.txt
    232 kB
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