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 training_args.bin from renjithks/layoutlmv3-cord-ner: direct link, hf CLI and curl.
- Browser
- Download file 3.18 kB
-
https://huggingface.co/renjithks/layoutlmv3-cord-ner/resolve/main/training_args.bin
- Command line
-
hf download hf://renjithks/layoutlmv3-cord-ner/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/renjithks/layoutlmv3-cord-ner/resolve/main/training_args.bin
3.18 kB
- Xet hash:
- cf4a08148e8d40741cc8832b53b2873e6119055549004b301027b23dd2ccbefe
- Size of remote file:
- 3.18 kB
- SHA256:
- ca9a147e0589c6baa23daec65cea0f95c83ddb3034d7e96949fd589ec9877621
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