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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Add evaluation results on emotion dataset
Beep boop, I am a bot from Hugging Face's automatic evaluation service!\nYour model has been evaluated on the [emotion](https://huggingface.co/datasets/emotion) dataset by @lewtun, using the predictions stored [here](https://huggingface.co/datasets/autoevaluate/autoeval-staging-eval-project-9ce2bb6d-db04-4bd1-97e7-18685ae940fd-4741). Accept this pull request to see the results displayed on the [Hub leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=emotion). Evaluate your model on more datasets [here](https://huggingface.co/spaces/autoevaluate/autoevaluate?dataset=emotion).44b5db7
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Add evaluation results on emotion (#7)
- Add evaluation results on emotion (4df6eb33830d6674b2c10863c146f2640f7bc4c2)79771c0
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update model card README.md
4b272e4