metadata
tags:
- spacy
- token-classification
language:
- es
license: mit
model-index:
- name: es_pharmaconer_ner_trf
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9066736184
- name: NER Recall
type: recall
value: 0.9152631579
- name: NER F Score
type: f_score
value: 0.9109481404
widget:
- text: >-
Se realizó estudio analítico destacando incremento de niveles de PTH y
vitamina D (103,7 pg/ml y 272 ng/ml, respectivamente), atribuidos al
exceso de suplementación de vitamina D.
- text: >-
Por el hallazgo de múltiples fracturas por estrés, se procedió a estudio
en nuestras consultas, realizándose análisis con función renal, calcio
sérico y urinario, calcio iónico, magnesio y PTH, que fueron normales.
- text: >-
Se solicitó una analítica que incluía hemograma, bioquímica, anticuerpos
antinucleares (ANA) y serologías, examen de orina, así como biopsia de la
lesión. Los resultados fueron normales, con ANA, anti-Sm, anti-RNP,
anti-SSA, anti-SSB, anti-Jo1 y anti-Scl70 negativos.
Basic Spacy BioNER pipeline, with a RoBERTa-based model [bsc-bio-ehr-es] (https://huggingface.co/PlanTL-GOB-ES/bsc-bio-ehr-es) and a dataset, Pharmaconer, a NER dataset annotated with substances, compounds and proteins entities. For further information, check the official website. Visit our GitHub repository. This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL
| Feature | Description |
|---|---|
| Name | es_pharmaconer_ner_trf |
| Version | 3.4.1 |
| spaCy | >=3.4.1,<3.5.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | mit |
| Author | The Text Mining Unit from Barcelona Supercomputing Center. |
| Copyright | Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022) |
| Funding | This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL |
Label Scheme
View label scheme (4 labels for 1 components)
| Component | Labels |
|---|---|
ner |
NORMALIZABLES, NO_NORMALIZABLES, PROTEINAS, UNCLEAR |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
91.09 |
ENTS_P |
90.67 |
ENTS_R |
91.53 |
TRANSFORMER_LOSS |
15719.51 |
NER_LOSS |
22469.88 |