Instructions to use vsalamand/fr_ner_ingredients with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use vsalamand/fr_ner_ingredients with spaCy:
!pip install https://huggingface.co/vsalamand/fr_ner_ingredients/resolve/main/fr_ner_ingredients-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("fr_ner_ingredients") # Importing as module. import fr_ner_ingredients nlp = fr_ner_ingredients.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | fr_ner_ingredients |
| 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 (5 labels for 1 components)
| Component | Labels |
|---|---|
ner |
BRAND, FOOD PRODUCT, INGREDIENT, MEASURE, QUANTITY |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
90.05 |
ENTS_P |
89.90 |
ENTS_R |
90.20 |
TOK2VEC_LOSS |
65769.53 |
NER_LOSS |
7865.95 |
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Evaluation results
- NER Precisionself-reported0.899
- NER Recallself-reported0.902
- NER F Scoreself-reported0.900