Instructions to use bsoviedo/es_COLNER_uncleaned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use bsoviedo/es_COLNER_uncleaned with spaCy:
!pip install https://huggingface.co/bsoviedo/es_COLNER_uncleaned/resolve/main/es_COLNER_uncleaned-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("es_COLNER_uncleaned") # Importing as module. import es_COLNER_uncleaned nlp = es_COLNER_uncleaned.load() - Notebooks
- Google Colab
- Kaggle
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Download README.md from bsoviedo/es_COLNER_uncleaned: direct link, hf CLI and curl.
- Browser
- Download file 1.13 kB
-
https://huggingface.co/bsoviedo/es_COLNER_uncleaned/resolve/main/README.md
- Command line
-
hf download hf://bsoviedo/es_COLNER_uncleaned/README.md
-
curl -L -o README.md https://huggingface.co/bsoviedo/es_COLNER_uncleaned/resolve/main/README.md
1.13 kB
metadata
tags:
- spacy
- token-classification
language:
- es
model-index:
- name: es_COLNER_uncleaned
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.960591133
- name: NER Recall
type: recall
value: 0.975
- name: NER F Score
type: f_score
value: 0.9677419355
| Feature | Description |
|---|---|
| Name | es_COLNER_uncleaned |
| Version | 0.0.0 |
| spaCy | >=3.8.4,<3.9.0 |
| 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 (1 labels for 1 components)
| Component | Labels |
|---|---|
ner |
LOC |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
96.77 |
ENTS_P |
96.06 |
ENTS_R |
97.50 |
TRANSFORMER_LOSS |
2051.59 |
NER_LOSS |
8819.29 |