Instructions to use Bharath18/en_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use Bharath18/en_pipeline with spaCy:
!pip install https://huggingface.co/Bharath18/en_pipeline/resolve/main/en_pipeline-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_pipeline") # Importing as module. import en_pipeline nlp = en_pipeline.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_pipeline |
| Version | 0.0.0 |
| spaCy | >=3.6.0,<3.7.0 |
| Default Pipeline | tok2vec, entity_ruler, ner |
| Components | tok2vec, entity_ruler, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (6 labels for 2 components)
| Component | Labels |
|---|---|
entity_ruler |
STATE, STATE_CODE, ZIPCODE |
ner |
CITY, STATE, VERTICAL |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
99.64 |
ENTS_P |
99.46 |
ENTS_R |
99.82 |
TOK2VEC_LOSS |
748.20 |
NER_LOSS |
227.50 |
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Evaluation results
- NER Precisionself-reported0.995
- NER Recallself-reported0.998
- NER F Scoreself-reported0.996