Token Classification
Transformers
PyTorch
Latin
xlm-roberta
Medieval Latin
Medieval Names
Latin NER
Latin-Vernacular
Instructions to use efontes/efontes-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use efontes/efontes-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="efontes/efontes-ner")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("efontes/efontes-ner") model = AutoModelForTokenClassification.from_pretrained("efontes/efontes-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from efontes/efontes-ner: direct link, hf CLI and curl.
- Browser
- Download file 4.47 GB
-
https://huggingface.co/efontes/efontes-ner/resolve/refs%2Fpr%2F2/optimizer.pt
- Command line
-
hf download hf://efontes/efontes-ner@refs/pr/2/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/efontes/efontes-ner/resolve/refs%2Fpr%2F2/optimizer.pt
4.47 GB
- Xet hash:
- cfa2432425622244e560a2700adafa0eff970af44969deb07caa929b653a7dbb
- Size of remote file:
- 4.47 GB
- SHA256:
- e2632edf5c3e475ffeb1fd26d8005ac46fa965d3a6deb03c06885b82b6127766
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.