Instructions to use Erni12322/en_lar_solidos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use Erni12322/en_lar_solidos with spaCy:
!pip install https://huggingface.co/Erni12322/en_lar_solidos/resolve/main/en_lar_solidos-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_lar_solidos") # Importing as module. import en_lar_solidos nlp = en_lar_solidos.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_lar_solidos |
| Version | 0.0.0 |
| spaCy | >=3.7.4,<3.8.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | 514157 keys, 514157 unique vectors (300 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (9 labels for 1 components)
| Component | Labels |
|---|---|
ner |
CLASSIFICACIÓN, ESPESOR/CALIBRE, LONGITUD, MATERIAL, NORMA, PESO ROLLO, PRESENTACION, TIPO, TRATAMIENTO |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
99.27 |
ENTS_P |
99.42 |
ENTS_R |
99.13 |
TOK2VEC_LOSS |
0.00 |
NER_LOSS |
0.00 |
- Downloads last month
- 6
Evaluation results
- NER Precisionself-reported0.994
- NER Recallself-reported0.991
- NER F Scoreself-reported0.993