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:
- 5342bf279d92298e85ec7d207e2458741a94a393b64935bd6bd2b0ecdf8f71bb
- Size of remote file:
- 4.03 kB
- SHA256:
- d86b6e379fc337d2a68fb5682732b33e54e6fc5fbfd7c5fc313e22793def79a3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.