Instructions to use bsoviedo/es_colner_added_tw_nl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use bsoviedo/es_colner_added_tw_nl with spaCy:
!pip install https://huggingface.co/bsoviedo/es_colner_added_tw_nl/resolve/main/es_colner_added_tw_nl-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("es_colner_added_tw_nl") # Importing as module. import es_colner_added_tw_nl nlp = es_colner_added_tw_nl.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | es_COLNER_added_tw_NL |
| Version | 0.0.0 |
| spaCy | >=3.8.4,<3.9.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (1 labels for 1 components)
| Component | Labels |
|---|---|
ner |
LOC |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
86.92 |
ENTS_P |
87.45 |
ENTS_R |
86.40 |
TRANSFORMER_LOSS |
11147.80 |
NER_LOSS |
17874.86 |
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Evaluation results
- NER Precisionself-reported0.874
- NER Recallself-reported0.864
- NER F Scoreself-reported0.869