Instructions to use krotzz/en_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use krotzz/en_pipeline with spaCy:
!pip install https://huggingface.co/krotzz/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 | 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 (26 labels for 1 components)
| Component | Labels |
|---|---|
ner |
ANGLE, CHEMICAL TERM, COUNTABLE, DECIMAL, EQU, EQUATION CITATION, FIGURE CITATION, FRACTION, GREEK VARIABLE, LEADING ZERO, NAMEDATE REF. CITATION, NUMBER, OPERATOR, ORDINAL, ORIENTATION, PERCENTAGE, RANGE, RATIO, ROMAN NUMBER, SI UNIT, SI UNIT , TABLE CITATION, THOUSANDS OPERATOR, THOUSANDS SEPARATOR, TIME UNIT, YEAR |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
98.38 |
ENTS_P |
98.48 |
ENTS_R |
98.28 |
TOK2VEC_LOSS |
34785.41 |
NER_LOSS |
84008.34 |
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Evaluation results
- NER Precisionself-reported0.985
- NER Recallself-reported0.983
- NER F Scoreself-reported0.984