Instructions to use reflex-project/fr_trf_ner_base_reflex_nrp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use reflex-project/fr_trf_ner_base_reflex_nrp with spaCy:
!pip install https://huggingface.co/reflex-project/fr_trf_ner_base_reflex_nrp/resolve/main/fr_trf_ner_base_reflex_nrp-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("fr_trf_ner_base_reflex_nrp") # Importing as module. import fr_trf_ner_base_reflex_nrp nlp = fr_trf_ner_base_reflex_nrp.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | fr_trf_ner_base_reflex_nrp |
| Version | 1.0.0 |
| spaCy | >=3.8.3,<3.9.0 |
| Default Pipeline | ner_transformer, ner |
| Components | ner_transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (3 labels for 1 components)
| Component | Labels |
|---|---|
ner |
LOC, ORG, PER |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
97.83 |
ENTS_P |
97.99 |
ENTS_R |
97.66 |
NER_TRANSFORMER_LOSS |
17821.85 |
NER_LOSS |
14488.08 |
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Evaluation results
- NER Precisionself-reported0.980
- NER Recallself-reported0.977
- NER F Scoreself-reported0.978