Instructions to use enelpe/en_test_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use enelpe/en_test_pipeline with spaCy:
!pip install https://huggingface.co/enelpe/en_test_pipeline/resolve/main/en_test_pipeline-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_test_pipeline") # Importing as module. import en_test_pipeline nlp = en_test_pipeline.load() - Notebooks
- Google Colab
- Kaggle
A test pipeline for ModelManager testing purposes
| Feature | Description |
|---|---|
| Name | en_test_pipeline |
| Version | 0.1.0 |
| spaCy | >=3.6.1,<3.7.0 |
| Default Pipeline | tok2vec, spancat |
| Components | tok2vec, spancat |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | MIT |
| Author | Liam Keegan |
Label Scheme
View label scheme (6 labels for 1 components)
| Component | Labels |
|---|---|
spancat |
Care, Fairness, Cheating, Oppression, Liberty, Harm |
Accuracy
| Type | Score |
|---|---|
SPANS_SC_F |
0.00 |
SPANS_SC_P |
0.00 |
SPANS_SC_R |
0.00 |
TOK2VEC_LOSS |
12290.20 |
SPANCAT_LOSS |
273866.65 |
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