Instructions to use khanzaid/en_seprate_industry with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use khanzaid/en_seprate_industry with spaCy:
!pip install https://huggingface.co/khanzaid/en_seprate_industry/resolve/main/en_seprate_industry-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_seprate_industry") # Importing as module. import en_seprate_industry nlp = en_seprate_industry.load() - Notebooks
- Google Colab
- Kaggle
second model almost included all ssector
| Feature | Description |
|---|---|
| Name | en_seprate_industry |
| Version | 0.0.0 |
| spaCy | >=3.7.4,<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 | zaid khan |
Label Scheme
View label scheme (1 labels for 1 components)
| Component | Labels |
|---|---|
ner |
industry |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
99.73 |
ENTS_P |
100.00 |
ENTS_R |
99.46 |
TOK2VEC_LOSS |
3238.79 |
NER_LOSS |
909.66 |
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Evaluation results
- NER Precisionself-reported1.000
- NER Recallself-reported0.995
- NER F Scoreself-reported0.997