Instructions to use Digiquanta/Document_Class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Digiquanta/Document_Class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Digiquanta/Document_Class")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("Digiquanta/Document_Class") model = AutoModelForSequenceClassification.from_pretrained("Digiquanta/Document_Class") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e6fdfe2f75dbd666d60953cf32821fd18c5a185a5e97e6955da1f1d286b2bfd7
- Size of remote file:
- 504 MB
- SHA256:
- a6d145343a2a7bccdbabe291d798ae76dac1c3f68aaa8502d769732dde9a0276
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