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 training_args.bin from tkazusa/lilt-en-funsd-org: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/tkazusa/lilt-en-funsd-org/resolve/main/training_args.bin
- Command line
-
hf download hf://tkazusa/lilt-en-funsd-org/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/tkazusa/lilt-en-funsd-org/resolve/main/training_args.bin
3.45 kB
- Xet hash:
- e92cec273bc64441a7c9bcc8f38a06833de27e26b2506f80619a35c8ec00b706
- Size of remote file:
- 3.45 kB
- SHA256:
- 209fd4650719ee8b107b126fb63543c334e42a434ceaa9756c2770b6a20f3aa3
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