metadata
tags:
- spacy
- token-classification
language:
- es
license: mit
model-index:
- name: es_neg_uncert_ehr_ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8964797136
- name: NER Recall
type: recall
value: 0.8997005988
- name: NER F Score
type: f_score
value: 0.8980872684
library_name: spacy
pipeline_tag: token-classification
| Feature | Description |
|---|---|
| Name | es_neg_uncert_ehr_ner |
| Version | 0.0.0 |
| spaCy | >=3.7.2,<3.8.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | mit |
| Author | Álvaro García Barragán |
Label Scheme
View label scheme (4 labels for 1 components)
| Component | Labels |
|---|---|
ner |
NEG, NSCO, UNC, USCO |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
89.81 |
ENTS_P |
89.65 |
ENTS_R |
89.97 |
TRANSFORMER_LOSS |
34598.52 |
NER_LOSS |
35036.89 |
Citation
If you use our work in your research, please cite it as follows:
@INPROCEEDINGS{garcia-barraganCBMS2023,
author={García-Barragán, Alvaro and Solarte-Pabón, Oswaldo and Nedostup, Georgiy and Provencio, Mariano and Menasalvas, Ernestina and Robles, Victor},
booktitle={2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS)},
title={Structuring Breast Cancer Spanish Electronic Health Records Using Deep Learning},
year={2023},
pages={404-409},
keywords={Natural Language Processing (NLP), Information extraction, Deep Learning, Breast cancer.},
doi={10.1109/CBMS58004.2023.00252}
}
Installing
!pip install pip==22.0.2
!pip install https://huggingface.co/Alvaro8gb/es_neg_uncert_ehr_ner/resolve/main/es_neg_uncert_ehr_ner-any-py3-none-any.whl
Dataset
Corpus composed of 29,682 sentences obtained from anonymised health records annotated with negation and uncertainty.
@article{lima2020nubes,
title={NUBes: A corpus of negation and uncertainty in Spanish clinical texts},
author={Lima, Salvador and Perez, Naiara and Cuadros, Montse and Rigau, German},
journal={arXiv preprint arXiv:2004.01092},
year={2020}
}