Instructions to use spacy/xx_ent_wiki_sm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use spacy/xx_ent_wiki_sm with spaCy:
!pip install https://huggingface.co/spacy/xx_ent_wiki_sm/resolve/main/xx_ent_wiki_sm-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("xx_ent_wiki_sm") # Importing as module. import xx_ent_wiki_sm nlp = xx_ent_wiki_sm.load() - Notebooks
- Google Colab
- Kaggle
Details: https://spacy.io/models/xx#xx_ent_wiki_sm
Multi-language pipeline optimized for CPU. Components: ner.
| Feature | Description |
|---|---|
| Name | xx_ent_wiki_sm |
| Version | 3.7.0 |
| spaCy | >=3.7.0,<3.8.0 |
| Default Pipeline | ner |
| Components | ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | WikiNER (Joel Nothman, Nicky Ringland, Will Radford, Tara Murphy, James R Curran) |
| License | MIT |
| Author | Explosion |
Label Scheme
View label scheme (4 labels for 1 components)
| Component | Labels |
|---|---|
ner |
LOC, MISC, ORG, PER |
Accuracy
| Type | Score |
|---|---|
ENTS_P |
83.53 |
ENTS_R |
82.65 |
ENTS_F |
83.08 |
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Evaluation results
- NER Precisionself-reported0.835
- NER Recallself-reported0.826
- NER F Scoreself-reported0.831