Instructions to use bsoviedo/es_COLNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use bsoviedo/es_COLNER with spaCy:
!pip install https://huggingface.co/bsoviedo/es_COLNER/resolve/main/es_COLNER-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("es_COLNER") # Importing as module. import es_COLNER nlp = es_COLNER.load() - Notebooks
- Google Colab
- Kaggle
Download meta.json from bsoviedo/es_COLNER: direct link, hf CLI and curl.
- Browser
- Download file 812 Bytes
-
https://huggingface.co/bsoviedo/es_COLNER/resolve/main/meta.json
- Command line
-
hf download hf://bsoviedo/es_COLNER/meta.json
-
curl -L -o meta.json https://huggingface.co/bsoviedo/es_COLNER/resolve/main/meta.json
812 Bytes
| { | |
| "lang":"es", | |
| "name":"COLNER", | |
| "version":"0.0.0", | |
| "description":"", | |
| "author":"", | |
| "email":"", | |
| "url":"", | |
| "license":"", | |
| "spacy_version":">=3.7.5,<3.8.0", | |
| "spacy_git_version":"a6d0fc360", | |
| "vectors":{ | |
| "width":300, | |
| "vectors":500000, | |
| "keys":500000, | |
| "name":"es_vectors" | |
| }, | |
| "labels":{ | |
| "tok2vec":[ | |
| ], | |
| "ner":[ | |
| "LOC" | |
| ] | |
| }, | |
| "pipeline":[ | |
| "tok2vec", | |
| "ner" | |
| ], | |
| "components":[ | |
| "tok2vec", | |
| "ner" | |
| ], | |
| "disabled":[ | |
| ], | |
| "performance":{ | |
| "ents_f":0.9170984456, | |
| "ents_p":0.9414893617, | |
| "ents_r":0.8939393939, | |
| "ents_per_type":{ | |
| "LOC":{ | |
| "p":0.9414893617, | |
| "r":0.8939393939, | |
| "f":0.9170984456 | |
| } | |
| }, | |
| "tok2vec_loss":29.4178245157, | |
| "ner_loss":387.3845205719 | |
| }, | |
| "requirements":[ | |
| ] | |
| } |