Instructions to use prof144/en_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use prof144/en_pipeline with spaCy:
!pip install https://huggingface.co/prof144/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 | >=3.7.4,<3.8.0 |
| 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 (11 labels for 1 components)
| Component | Labels |
|---|---|
ner |
College Name, Companies worked at, Degree, Designation, Email Address, Graduation Year, Location, Name, Skills, UNKNOWN, Years of Experience |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
60.22 |
ENTS_P |
59.11 |
ENTS_R |
61.37 |
TRANSFORMER_LOSS |
26200.68 |
NER_LOSS |
290652.50 |
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Space using prof144/en_pipeline 1
Evaluation results
- NER Precisionself-reported0.591
- NER Recallself-reported0.614
- NER F Scoreself-reported0.602