Instructions to use ivorpad/en_fine_tuned_en_skillner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use ivorpad/en_fine_tuned_en_skillner with spaCy:
!pip install https://huggingface.co/ivorpad/en_fine_tuned_en_skillner/resolve/main/en_fine_tuned_en_skillner-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_fine_tuned_en_skillner") # Importing as module. import en_fine_tuned_en_skillner nlp = en_fine_tuned_en_skillner.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_fine_tuned_en_skillner |
| Version | 0.0.1 |
| spaCy | >=3.8.4,<3.9.0 |
| 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 (1 labels for 1 components)
| Component | Labels |
|---|---|
ner |
SKILL |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
100.00 |
ENTS_P |
100.00 |
ENTS_R |
100.00 |
TOK2VEC_LOSS |
0.00 |
NER_LOSS |
0.00 |
- Downloads last month
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Evaluation results
- NER Precisionself-reported1.000
- NER Recallself-reported1.000
- NER F Scoreself-reported1.000