Instructions to use JeswinMS4/en_resume_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use JeswinMS4/en_resume_pipeline with spaCy:
!pip install https://huggingface.co/JeswinMS4/en_resume_pipeline/resolve/main/en_resume_pipeline-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_resume_pipeline") # Importing as module. import en_resume_pipeline nlp = en_resume_pipeline.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_resume_pipeline |
| Version | 0.0.0 |
| spaCy | >=3.7.5,<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 (18 labels for 1 components)
| Component | Labels |
|---|---|
ner |
AWARDS, CERTIFICATION, COLLEGE NAME, COMPANIES WORKED AT, CONTACT, DEGREE, DESIGNATION, EMAIL ADDRESS, LANGUAGE, LINKEDIN LINK, LOCATION, NAME, SKILLS, UNIVERSITY, Unlabelled, WORKED AS, YEAR OF GRADUATION, YEARS OF EXPERIENCE |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
86.03 |
ENTS_P |
85.71 |
ENTS_R |
86.35 |
TRANSFORMER_LOSS |
396847.27 |
NER_LOSS |
525302.40 |
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Evaluation results
- NER Precisionself-reported0.857
- NER Recallself-reported0.863
- NER F Scoreself-reported0.860