Instructions to use pnichite/en_pipeline_123 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use pnichite/en_pipeline_123 with spaCy:
!pip install https://huggingface.co/pnichite/en_pipeline_123/resolve/main/en_pipeline_123-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_pipeline_123") # Importing as module. import en_pipeline_123 nlp = en_pipeline_123.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_pipeline_123 |
| Version | 0.0.0 |
| spaCy | >=3.4.1,<3.5.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (2 labels for 1 components)
| Component | Labels |
|---|---|
ner |
DESCRIPTION, TITLE |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
78.52 |
ENTS_P |
76.62 |
ENTS_R |
80.52 |
TRANSFORMER_LOSS |
1811559.14 |
NER_LOSS |
6345113.13 |
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Evaluation results
- NER Precisionself-reported0.766
- NER Recallself-reported0.805
- NER F Scoreself-reported0.785