Instructions to use jfrei/de_GERNERMEDpp_GermanBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use jfrei/de_GERNERMEDpp_GermanBERT with spaCy:
!pip install https://huggingface.co/jfrei/de_GERNERMEDpp_GermanBERT/resolve/main/de_GERNERMEDpp_GermanBERT-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("de_GERNERMEDpp_GermanBERT") # Importing as module. import de_GERNERMEDpp_GermanBERT nlp = de_GERNERMEDpp_GermanBERT.load() - Notebooks
- Google Colab
- Kaggle
GottBERT-based model of the GERNERMED++ German NER model for medical entities.
| Feature | Description |
|---|---|
| Name | de_GERNERMEDpp_GermanBERT |
| Version | 1.0.0 |
| spaCy | >=3.2.3,<3.3.0 |
| 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 (6 labels for 1 components)
| Component | Labels |
|---|---|
ner |
Dosage, Drug, Duration, Form, Frequency, Strength |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
90.87 |
ENTS_P |
90.89 |
ENTS_R |
90.86 |
TRANSFORMER_LOSS |
193600.80 |
NER_LOSS |
255416.71 |
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Evaluation results
- NER Precisionself-reported0.909
- NER Recallself-reported0.909
- NER F Scoreself-reported0.909