metadata
tags:
- spacy
- token-classification
language:
- de
widget:
- text: Mein Asthma behandle ich mit 10mg Salbutamol.
model-index:
- name: de_GPTNERMED_gbert
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9022055764
- name: NER Recall
type: recall
value: 0.9209855565
- name: NER F Score
type: f_score
value: 0.9114988438
Gbert-based model of the GPTNERMED German NER model for medical entities.
See our published paper at: https://doi.org/10.1016/j.jbi.2023.104478
The preprint paper is available at: https://arxiv.org/abs/2208.14493
If you like our work, give us a star on our GitHub repository: https://github.com/frankkramer-lab/GPTNERMED
| Feature | Description |
|---|---|
| Name | de_GPTNERMED_gbert |
| Version | 1.0.0 |
| 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 | n/a |
| Author | Johann Frei |
Label Scheme
View label scheme (3 labels for 1 components)
| Component | Labels |
|---|---|
ner |
Diagnose, Dosis, Medikation |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
91.15 |
ENTS_P |
90.22 |
ENTS_R |
92.10 |
TRANSFORMER_LOSS |
32882.59 |
NER_LOSS |
56921.35 |