Instructions to use Tommycl/en_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use Tommycl/en_pipeline with spaCy:
!pip install https://huggingface.co/Tommycl/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 | >=3.7.5,<3.8.0 |
| 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 (8 labels for 1 components)
| Component | Labels |
|---|---|
ner |
art, eve, geo, gpe, nat, org, per, tim |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
84.64 |
ENTS_P |
85.05 |
ENTS_R |
84.24 |
TRANSFORMER_LOSS |
395372.62 |
NER_LOSS |
568903.99 |
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Evaluation results
- NER Precisionself-reported0.851
- NER Recallself-reported0.842
- NER F Scoreself-reported0.846