Text Classification
Transformers
PyTorch
TensorBoard
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use autoevaluate/binary-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use autoevaluate/binary-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="autoevaluate/binary-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("autoevaluate/binary-classification") model = AutoModelForSequenceClassification.from_pretrained("autoevaluate/binary-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c2d5c2cbc0ddf7dd649027e7b87318672f35239374708899fab56d8ac5cf803b
- Size of remote file:
- 268 MB
- SHA256:
- 963067aead7d42427a082cf1ba9c04992c734ab55a4707e1d9518c6011a6e10b
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