Instructions to use Innocull/en_gendered_job_advertisements with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use Innocull/en_gendered_job_advertisements with spaCy:
!pip install https://huggingface.co/Innocull/en_gendered_job_advertisements/resolve/main/en_gendered_job_advertisements-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_gendered_job_advertisements") # Importing as module. import en_gendered_job_advertisements nlp = en_gendered_job_advertisements.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_gendered_job_advertisements |
| Version | 0.0.0 |
| spaCy | >=3.3.1,<3.4.0 |
| 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 (2 labels for 1 components)
| Component | Labels |
|---|---|
ner |
FEM, MAS |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
99.31 |
ENTS_P |
99.65 |
ENTS_R |
98.98 |
TOK2VEC_LOSS |
877.83 |
NER_LOSS |
897.35 |
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Evaluation results
- NER Precisionself-reported0.996
- NER Recallself-reported0.990
- NER F Scoreself-reported0.993