Instructions to use easyh/de_fnhd_nerdh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use easyh/de_fnhd_nerdh with spaCy:
!pip install https://huggingface.co/easyh/de_fnhd_nerdh/resolve/main/de_fnhd_nerdh-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("de_fnhd_nerdh") # Importing as module. import de_fnhd_nerdh nlp = de_fnhd_nerdh.load() - Notebooks
- Google Colab
- Kaggle
Deutsche NER-Pipeline für frühneuhochdeutsche Texte (2.Version)
| Feature | Description |
|---|---|
| Name | de_fnhd_nerdh |
| Version | 0.0.2 |
| spaCy | >=3.4.1,<3.5.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | 500000 keys, 500000 unique vectors (300 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | ih |
Label Scheme
View label scheme (5 labels for 1 components)
| Component | Labels |
|---|---|
ner |
OBJEKT, ORGANISATION, ORT, PERSON, ZEIT |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
95.66 |
ENTS_P |
96.29 |
ENTS_R |
95.04 |
TOK2VEC_LOSS |
25311.59 |
NER_LOSS |
15478.32 |
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Evaluation results
- NER Precisionself-reported0.963
- NER Recallself-reported0.950
- NER F Scoreself-reported0.957