French model for information extraction from Job postings
Feature | Description |
---|---|
Name | fr_job_info_extr_fr |
Version | 0.0.0 |
spaCy | >=3.5.1,<3.6.0 |
Default Pipeline | tok2vec , ner |
Components | tok2vec , ner |
Vectors | 500000 keys, 500000 unique vectors (300 dimensions) |
Sources | n/a |
License | n/a |
Author | Youssef Chafiqui |
Label Scheme
View label scheme (8 labels for 1 components)
Component | Labels |
---|---|
ner |
CONTRAT , EDUCATION , EXPERIENCE , HARD-SKILL , LANGUE , POSTE , SALAIRE , SOFT-SKILL |
Accuracy
Type | Score |
---|---|
ENTS_F |
96.10 |
ENTS_P |
95.98 |
ENTS_R |
96.22 |
TOK2VEC_LOSS |
219378.28 |
NER_LOSS |
68755.91 |
Usage
Presequities
Install spaCy library
pip install spacy
Download the model
pip install https://huggingface.co/ychafiqui/fr_job_info_extr_fr/resolve/main/fr_job_info_extr_fr-any-py3-none-any.whl
Load the model
import spacy
nlp = spacy.load("fr_job_info_extr_fr")
Inference using the model
doc = nlp('put your job description here')
for ent in doc.ents:
print(ent.text, "-", ent.label_)
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Evaluation results
- NER Precisionself-reported0.960
- NER Recallself-reported0.962
- NER F Scoreself-reported0.961