| Feature | Description |
|---|---|
| Name | en_SkillExtraction |
| Version | 0.0.0 |
| spaCy | >=3.5.3,<3.6.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | 514157 keys, 514157 unique vectors (300 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (8 labels for 1 components)
| Component | Labels |
|---|---|
ner |
DESIGNATION, EDUCATION, EMAIL, LANGUAGE, NAME, PHONE, PLACE, SKILL |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
96.22 |
ENTS_P |
95.14 |
ENTS_R |
97.33 |
TOK2VEC_LOSS |
15547.71 |
NER_LOSS |
105573.97 |
- Downloads last month
- -
Evaluation results
- NER Precisionself-reported0.951
- NER Recallself-reported0.973
- NER F Scoreself-reported0.962
!pip install https://huggingface.co/MohammedShaneeb/en_SkillExtraction/resolve/main/en_SkillExtraction-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_SkillExtraction") # Importing as module. import en_SkillExtraction nlp = en_SkillExtraction.load()