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mdeberta-v3-base-ehealth_kd
IIC/mdeberta-v3-base-ehealth_kd
mdeberta-v3-base-ehealth_kd is a token classification model from IIC. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.
This model is a finetuned version of mdeberta-v3-base for the eHealth-KD dataset used in a benchmark in the paper A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks. The mo…
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From the Hugging Face model README
mdeberta-v3-base-ehealth_kd
This model is a finetuned version of mdeberta-v3-base for the eHealth-KD dataset used in a benchmark in the paper A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks. The model has a F1 of 0.844
Please refer to the original publication for more information.
Parameters used
| parameter | Value |
|---|---|
| batch size | 32 |
| learning rate | 3e-05 |
| classifier dropout | 0.2 |
| warmup ratio | 0 |
| warmup steps | 0 |
| weight decay | 0 |
| optimizer | AdamW |
| epochs | 10 |
| early stopping patience | 3 |
BibTeX entry and citation info
@article{10.1093/jamia/ocae054,
author = {García Subies, Guillem and Barbero Jiménez, Álvaro and Martínez Fernández, Paloma},
title = {A comparative analysis of Spanish Clinical encoder-based models on NER and classification tasks},
journal = {Journal of the American Medical Informatics Association},
volume = {31},
number = {9},
pages = {2137-2146},
year = {2024},
month = {03},
issn = {1527-974X},
doi = {10.1093/jamia/ocae054},
url = {https://doi.org/10.1093/jamia/ocae054},
}
- Task
- Token Classification
- Library
- transformers
- Type
- deberta-v2
- License
- mit
- Languages
- es
- Created
- Jun 21, 2023
- Updated
- Nov 25, 2024