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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d93251cead74bcab6f3a60f9e9f4bfd9f643e313d38baa8d401096060aa82a49
- Size of remote file:
- 5.2 kB
- SHA256:
- 61b18d7379960487ae5be3a28fc27b4bda5fbcd05e444d952f2ba1aef9bca31c
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