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")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("autoevaluate/multi-class-classification") model = AutoModelForSequenceClassification.from_pretrained("autoevaluate/multi-class-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1ba14177b9ef2d21c643c7480ef85bc66a986b0a08604424b6a0ceaffd3dd807
- Size of remote file:
- 268 MB
- SHA256:
- 566e13f4ef0eecce8b2829d6c2309595779533db00682ee8e0e57dea9430c1f1
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