Instructions to use tkazusa/lilt-en-funsd-org with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tkazusa/lilt-en-funsd-org with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="tkazusa/lilt-en-funsd-org")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("tkazusa/lilt-en-funsd-org") model = AutoModelForTokenClassification.from_pretrained("tkazusa/lilt-en-funsd-org", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from tkazusa/lilt-en-funsd-org: direct link, hf CLI and curl.
- Browser
- Download file 521 MB
-
https://huggingface.co/tkazusa/lilt-en-funsd-org/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://tkazusa/lilt-en-funsd-org/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/tkazusa/lilt-en-funsd-org/resolve/main/pytorch_model.bin
521 MB
- Xet hash:
- 1b47d3f9c294c21575043311f9ee6539238227b98122d1551f264af74164ac46
- Size of remote file:
- 521 MB
- SHA256:
- 2b35fd9f5ec42800031fdc8176cf05c62c7e3d4ef03e7082d43a3f9998aaaa2b
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