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")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("vikp/column_detector") model = AutoModelForSequenceClassification.from_pretrained("vikp/column_detector", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vikp/column_detector: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/vikp/column_detector/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vikp/column_detector/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vikp/column_detector/resolve/main/pytorch_model.bin
504 MB
- Xet hash:
- 5363ef71536503b46eae2a6906fe0446537f5355d2a09caffa032c2f5b994905
- Size of remote file:
- 504 MB
- SHA256:
- 0533a601d298cb4a7e2c48aa575d0e3d0f105d6ddda8be7e0b5ba845dc93e7d3
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