Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use autoevaluate/multi-class-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/multi-class-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autoevaluate/multi-class-classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("autoevaluate/multi-class-classification") model = AutoModelForSequenceClassification.from_pretrained("autoevaluate/multi-class-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Test
This is a teste3a425f
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Add evaluation results on emotion (#7)
- Add evaluation results on emotion (4df6eb33830d6674b2c10863c146f2640f7bc4c2)79771c0
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Add label mapping (#1)
- Add label mapping (3a7d78994100e4df2166f81fa84a59b6d98e6b0a)0674dee -
update model card README.md
4b272e4 -
End of training
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Training in progress, epoch 1
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initial commit
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