Text Classification
Transformers
PyTorch
English
wrag2
weight-retrieval
domain-adaptation
medical
legal
code
Instructions to use Gyeti123/wrag2-text-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gyeti123/wrag2-text-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Gyeti123/wrag2-text-classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Gyeti123/wrag2-text-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d976ab5c2d9115e3ede6d2b1cfa42848bf0ee2d38eeeb7be33c75fd84bb392ba
- Size of remote file:
- 529 MB
- SHA256:
- 5a4bba9f25245c038d4ab991e3b17182a0084dff5cbcd75733a0a9b204eb5851
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