Instructions to use vsalamand/fr_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use vsalamand/fr_pipeline with spaCy:
!pip install https://huggingface.co/vsalamand/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 | >=3.2.1,<3.3.0 |
| 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 (4 labels for 1 components)
| Component | Labels |
|---|---|
ner |
FOOD PRODUCT, INGREDIENT, MEASURE, QUANTITY |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
91.33 |
ENTS_P |
90.11 |
ENTS_R |
92.58 |
TOK2VEC_LOSS |
8670.94 |
NER_LOSS |
4165.31 |
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Evaluation results
- NER Precisionself-reported0.901
- NER Recallself-reported0.926
- NER F Scoreself-reported0.913