Instructions to use vikp/column_detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vikp/column_detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vikp/column_detector")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("vikp/column_detector") model = AutoModelForSequenceClassification.from_pretrained("vikp/column_detector") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5363ef71536503b46eae2a6906fe0446537f5355d2a09caffa032c2f5b994905
- Size of remote file:
- 504 MB
- SHA256:
- 0533a601d298cb4a7e2c48aa575d0e3d0f105d6ddda8be7e0b5ba845dc93e7d3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.