Instructions to use npayaresc/lilt-en-funsd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use npayaresc/lilt-en-funsd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="npayaresc/lilt-en-funsd")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("npayaresc/lilt-en-funsd") model = AutoModelForTokenClassification.from_pretrained("npayaresc/lilt-en-funsd", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from npayaresc/lilt-en-funsd: direct link, hf CLI and curl.
- Browser
- Download file 3.44 kB
-
https://huggingface.co/npayaresc/lilt-en-funsd/resolve/main/training_args.bin
- Command line
-
hf download hf://npayaresc/lilt-en-funsd/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/npayaresc/lilt-en-funsd/resolve/main/training_args.bin
3.44 kB
- Xet hash:
- 44757ddf913f23c9e62c8e5543c6fd763951380b5a5078249687121b25d44f23
- Size of remote file:
- 3.44 kB
- SHA256:
- 732717340264209e54f0cd75c48b951540080db229531732f26b79370cf16af2
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