Instructions to use Docify-in/table-transformer-demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Docify-in/table-transformer-demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("object-detection", model="Docify-in/table-transformer-demo")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForObjectDetection processor = AutoImageProcessor.from_pretrained("Docify-in/table-transformer-demo") model = AutoModelForObjectDetection.from_pretrained("Docify-in/table-transformer-demo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Docify-in/table-transformer-demo: direct link, hf CLI and curl.
- Browser
- Download file 116 MB
-
https://huggingface.co/Docify-in/table-transformer-demo/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Docify-in/table-transformer-demo/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Docify-in/table-transformer-demo/resolve/main/pytorch_model.bin
116 MB
- Xet hash:
- ca0a95a1584860ed335cb3579424487617c65d64d976790ed5f94b2ecab38e14
- Size of remote file:
- 116 MB
- SHA256:
- a534f9e3f6edf1b230c742924e06f5e35c2c148a1c9c5615f73e5dad27272786
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