Token Classification
Transformers
Safetensors
xlm-roberta
ner
named-entity-recognition
clinical-ner
biomedical-ner
multilingual
Instructions to use BSC-NLP4BIA/DT4H_XLM-R_stl_multilingual_disease with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BSC-NLP4BIA/DT4H_XLM-R_stl_multilingual_disease with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="BSC-NLP4BIA/DT4H_XLM-R_stl_multilingual_disease")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("BSC-NLP4BIA/DT4H_XLM-R_stl_multilingual_disease") model = AutoModelForTokenClassification.from_pretrained("BSC-NLP4BIA/DT4H_XLM-R_stl_multilingual_disease") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f16a673c7a433cf00fec64fe25f51387a04a531e6bcfc91c1cb9e08c936dc031
- Size of remote file:
- 16.8 MB
- SHA256:
- 2464f9721707cb3d5edcf9a3d73454b13e8a7b3bb8fdba94b3de3d843f30e946
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.