Instructions to use nishantk613/en_Task2_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use nishantk613/en_Task2_pipeline with spaCy:
!pip install https://huggingface.co/nishantk613/en_Task2_pipeline/resolve/main/en_Task2_pipeline-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_Task2_pipeline") # Importing as module. import en_Task2_pipeline nlp = en_Task2_pipeline.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_Task2_pipeline |
| Version | 0.0.0 |
| spaCy | >=3.6.1,<3.7.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | 514157 keys, 514157 unique vectors (300 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (4 labels for 1 components)
| Component | Labels |
|---|---|
ner |
Allergy, Cancer, Chronic Disease, Treatment |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
91.47 |
ENTS_P |
91.47 |
ENTS_R |
91.48 |
TOK2VEC_LOSS |
40406.92 |
NER_LOSS |
667407.78 |
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Evaluation results
- NER Precisionself-reported0.915
- NER Recallself-reported0.915
- NER F Scoreself-reported0.915