Instructions to use FarhanQ101/en_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use FarhanQ101/en_pipeline with spaCy:
!pip install https://huggingface.co/FarhanQ101/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 | >=3.7.1,<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 (8 labels for 1 components)
| Component | Labels |
|---|---|
ner |
AMOUNT, AVAILABLE_BAL, DATE, OTHER_ACC_ADD, RECEPIENT_NAME, TIME, UPI_REF_ID, USER_ACC_ADD |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
99.70 |
ENTS_P |
99.70 |
ENTS_R |
99.70 |
TOK2VEC_LOSS |
8615.31 |
NER_LOSS |
9620.54 |
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Evaluation results
- NER Precisionself-reported0.997
- NER Recallself-reported0.997
- NER F Scoreself-reported0.997