Instructions to use NiamaLynn/lilt-roberta-DocLayNet-base_lines_ml256-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NiamaLynn/lilt-roberta-DocLayNet-base_lines_ml256-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="NiamaLynn/lilt-roberta-DocLayNet-base_lines_ml256-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("NiamaLynn/lilt-roberta-DocLayNet-base_lines_ml256-v1") model = AutoModelForTokenClassification.from_pretrained("NiamaLynn/lilt-roberta-DocLayNet-base_lines_ml256-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from NiamaLynn/lilt-roberta-DocLayNet-base_lines_ml256-v1: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/NiamaLynn/lilt-roberta-DocLayNet-base_lines_ml256-v1/resolve/main/tokenizer.json
- Command line
-
hf download hf://NiamaLynn/lilt-roberta-DocLayNet-base_lines_ml256-v1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/NiamaLynn/lilt-roberta-DocLayNet-base_lines_ml256-v1/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- cde0a739dfeb54adcaca8e8a7bad2f75f748e56e0140aaa34d3005874a8153ce
- Size of remote file:
- 17.1 MB
- SHA256:
- 1c20712be1220aa707f3804ed28a7d9a991011eb1d5748f4cbf20f9fdbdbb602
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.