Instructions to use aman9608/en_t2_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use aman9608/en_t2_pipeline with spaCy:
!pip install https://huggingface.co/aman9608/en_t2_pipeline/resolve/main/en_t2_pipeline-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_t2_pipeline") # Importing as module. import en_t2_pipeline nlp = en_t2_pipeline.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_t2_pipeline |
| Version | 0.0.0 |
| spaCy | >=3.7.2,<3.8.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (5 labels for 1 components)
| Component | Labels |
|---|---|
ner |
Other, allergy_name, cancer, chronic_disease, treatment |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
98.22 |
ENTS_P |
98.41 |
ENTS_R |
98.02 |
TOK2VEC_LOSS |
747277.39 |
NER_LOSS |
462952.49 |
- Downloads last month
- -
Evaluation results
- NER Precisionself-reported0.984
- NER Recallself-reported0.980
- NER F Scoreself-reported0.982