Instructions to use YousraBerrachedi/fr_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use YousraBerrachedi/fr_pipeline with spaCy:
!pip install https://huggingface.co/YousraBerrachedi/fr_pipeline/resolve/main/fr_pipeline-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("fr_pipeline") # Importing as module. import fr_pipeline nlp = fr_pipeline.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | fr_pipeline |
| Version | 0.0.0 |
| spaCy | >=3.4.4,<3.5.0 |
| 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 (8 labels for 1 components)
| Component | Labels |
|---|---|
ner |
EMOTICON, GOVERNING ENTITY, HASHTAG, LINK, NUMBERS/METRICS, QUESTION, REFERENCE, TAG |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
77.97 |
ENTS_P |
80.45 |
ENTS_R |
75.64 |
TOK2VEC_LOSS |
9744.43 |
NER_LOSS |
4349.46 |
- Downloads last month
- 2
Evaluation results
- NER Precisionself-reported0.805
- NER Recallself-reported0.756
- NER F Scoreself-reported0.780