Text Classification
Transformers
PyTorch
TensorBoard
roberta
Generated from Trainer
Eval Results (legacy)
Instructions to use Emanuel/bertweet-emotion-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Emanuel/bertweet-emotion-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Emanuel/bertweet-emotion-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Emanuel/bertweet-emotion-base") model = AutoModelForSequenceClassification.from_pretrained("Emanuel/bertweet-emotion-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator (#3)
- Add verifyToken field to verify evaluation results are produced by Hugging Face's automatic model evaluator (40ff8d63035c7d98aa4db5a3dbb083ddf427f46f) Co-authored-by: Evaluation Bot <autoevaluator@users.noreply.huggingface.co>ea602f5
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Add evaluation results on the default config and test split of emotion (#2)
- Add evaluation results on the default config and test split of emotion (2e74d97216866a45a1a9a5863e1c4824203395df) Co-authored-by: Evaluation Bot <autoevaluator@users.noreply.huggingface.co>082f4cb
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Align label mapping with emotion dataset (#1)
- Align label mapping with emotion dataset (c105051bd9de043c18b908beaa328c25f672f42b) Co-authored-by: Lewis Tunstall <lewtun@users.noreply.huggingface.co>7a6581a
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initial commit
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