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
File size: 657 Bytes
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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}}
```
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