Instructions to use vikp/layout_segmenter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vikp/layout_segmenter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vikp/layout_segmenter")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("vikp/layout_segmenter") model = AutoModelForTokenClassification.from_pretrained("vikp/layout_segmenter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from vikp/layout_segmenter: direct link, hf CLI and curl.
- Browser
- Download file 2.11 MB
-
https://huggingface.co/vikp/layout_segmenter/resolve/refs%2Fpr%2F3/tokenizer.json
- Command line
-
hf download hf://vikp/layout_segmenter@refs/pr/3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/vikp/layout_segmenter/resolve/refs%2Fpr%2F3/tokenizer.json
2.11 MB
File too large to display, you can check the raw version instead.