Feature Extraction
Transformers
PyTorch
Safetensors
Spanish
xlm-roberta
beto
galen
text-embeddings-inference
Instructions to use IIC/XLM-R_Galen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IIC/XLM-R_Galen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="IIC/XLM-R_Galen")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("IIC/XLM-R_Galen") model = AutoModel.from_pretrained("IIC/XLM-R_Galen", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| language: es | |
| tags: | |
| - beto | |
| - galen | |
| license: mit | |
| # XLM-R Galén | |
| This is a third party reupload of the original XLM-R Galén model, available in [GitHub](https://github.com/guilopgar/ClinicalCodingTransformerES). | |
| Please refer to the original publication for more information | |
| ## BibTeX entry and citation info | |
| ```bibtex | |
| @article{9430499, | |
| author={López-García, Guillermo and Jerez, José M. and Ribelles, Nuria and Alba, Emilio and Veredas, Francisco J.}, | |
| journal={IEEE Access}, | |
| title={Transformers for Clinical Coding in Spanish}, | |
| year={2021}, | |
| volume={9}, | |
| number={}, | |
| pages={72387-72397}, | |
| doi={10.1109/ACCESS.2021.3080085}} | |
| ``` | |