Instructions to use renjithks/layoutlmv3-cord-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use renjithks/layoutlmv3-cord-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="renjithks/layoutlmv3-cord-ner")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("renjithks/layoutlmv3-cord-ner") model = AutoModelForTokenClassification.from_pretrained("renjithks/layoutlmv3-cord-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from renjithks/layoutlmv3-cord-ner: direct link, hf CLI and curl.
- Browser
- Download file 501 MB
-
https://huggingface.co/renjithks/layoutlmv3-cord-ner/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://renjithks/layoutlmv3-cord-ner/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/renjithks/layoutlmv3-cord-ner/resolve/main/pytorch_model.bin
501 MB
- Xet hash:
- 840931abede5f1a615ef085771253c46e4c0992fa42165433b1d9b44fc39e72c
- Size of remote file:
- 501 MB
- SHA256:
- f8e2ec6d90243a3d9a68c6a555b3d132e9e96065e1e9858b859a6ddb23f48d4f
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