Token Classification
Transformers
Safetensors
Latin
German
bert
medieval
diplomatics
charter
knowledge-distillation
weakly-supervised
Instructions to use ERCDiDip/charter-diplomatic-segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ERCDiDip/charter-diplomatic-segmentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ERCDiDip/charter-diplomatic-segmentation")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ERCDiDip/charter-diplomatic-segmentation") model = AutoModelForTokenClassification.from_pretrained("ERCDiDip/charter-diplomatic-segmentation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ERCDiDip/charter-diplomatic-segmentation: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/ERCDiDip/charter-diplomatic-segmentation/resolve/main/tokenizer.json
- Command line
-
hf download hf://ERCDiDip/charter-diplomatic-segmentation/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ERCDiDip/charter-diplomatic-segmentation/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 83182779330bf02f2e8bedc27659fcb1bf597a905d3060f0bbdbeb663b289b43
- Size of remote file:
- 17.1 MB
- SHA256:
- b74659c780d49afad7a7b9799868f75cbd3014fb6c34956e85a793028d38094a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.