Instructions to use mgoermar/de_grand_tour_ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use mgoermar/de_grand_tour_ner with spaCy:
!pip install https://huggingface.co/mgoermar/de_grand_tour_ner/resolve/main/de_grand_tour_ner-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("de_grand_tour_ner") # Importing as module. import de_grand_tour_ner nlp = de_grand_tour_ner.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | de_grand_tour_ner |
| Version | 0.0.1 |
| spaCy | >=3.7.5,<3.8.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | 500000 keys, 500000 unique vectors (300 dimensions) |
| Sources | n/a |
| License | MIT license |
| Author | Maximilian Görmar |
Label Scheme
View label scheme (4 labels for 1 components)
| Component | Labels |
|---|---|
ner |
LOC, MISC, ORG, PER |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
74.84 |
ENTS_P |
67.79 |
ENTS_R |
83.54 |
TOK2VEC_LOSS |
10177.99 |
NER_LOSS |
642590.05 |
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Evaluation results
- NER Precisionself-reported67.790
- NER Recallself-reported83.540
- NER F Scoreself-reported74.840