Instructions to use hsengiv/en_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use hsengiv/en_pipeline with spaCy:
!pip install https://huggingface.co/hsengiv/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 | 0.1.0 |
| spaCy | >=3.3.1,<3.4.0 |
| Default Pipeline | transformer, textcat |
| Components | transformer, textcat |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (2 labels for 1 components)
| Component | Labels |
|---|---|
textcat |
negative, positive |
Accuracy
| Type | Score |
|---|---|
CATS_SCORE |
91.18 |
CATS_MICRO_P |
91.20 |
CATS_MICRO_R |
91.20 |
CATS_MICRO_F |
91.20 |
CATS_MACRO_P |
91.68 |
CATS_MACRO_R |
91.20 |
CATS_MACRO_F |
91.18 |
CATS_MACRO_AUC |
95.12 |
CATS_MACRO_AUC_PER_TYPE |
0.00 |
TRANSFORMER_LOSS |
863.63 |
TEXTCAT_LOSS |
5818.88 |
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