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 pytorch_model.bin from npayaresc/lilt-en-funsd: direct link, hf CLI and curl.
- Browser
- Download file 521 MB
-
https://huggingface.co/npayaresc/lilt-en-funsd/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://npayaresc/lilt-en-funsd/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/npayaresc/lilt-en-funsd/resolve/main/pytorch_model.bin
521 MB
- Xet hash:
- 7f0b456220b41a1b4d6fbf13da57e834a0a3d4da7dfbba82d6dc06fc2ac5a142
- Size of remote file:
- 521 MB
- SHA256:
- bddab7893cf7a8da54e14add544c21da9904aec63ef63b75ffcdf863782a5f6a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.