Instructions to use Jacobo/grc_ner_trf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use Jacobo/grc_ner_trf with spaCy:
!pip install https://huggingface.co/Jacobo/grc_ner_trf/resolve/main/grc_ner_trf-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("grc_ner_trf") # Importing as module. import grc_ner_trf nlp = grc_ner_trf.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | grc_ner_trf |
| Version | 3.7 |
| spaCy | >=3.7.4,<3.8.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (9 labels for 1 components)
| Component | Labels |
|---|---|
ner |
EVENT, GOD, GPE, LANGUAGE, LOC, NORP, ORG, PERSON, WORK |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
91.44 |
ENTS_P |
91.72 |
ENTS_R |
91.16 |
TRANSFORMER_LOSS |
34008.99 |
NER_LOSS |
48633.82 |
- Downloads last month
- 5
Evaluation results
- NER Precisionself-reported0.925
- NER Recallself-reported0.909
- NER F Scoreself-reported0.917