Text Classification
Transformers
PyTorch
distilbert
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use genaibook/classifier-chapter4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use genaibook/classifier-chapter4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="genaibook/classifier-chapter4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("genaibook/classifier-chapter4") model = AutoModelForSequenceClassification.from_pretrained("genaibook/classifier-chapter4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8a4ed6e8f9dbaa86091afa9e55f537857add3539f1052992c649136be1f83bad
- Size of remote file:
- 268 MB
- SHA256:
- c520a75ae4435366d26542f770189eb65a9b76a229a25872e008c746f0d49b14
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