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
Download meta.json from bsoviedo/es_COLNER_uncleaned: direct link, hf CLI and curl.
- Browser
- Download file 890 Bytes
-
https://huggingface.co/bsoviedo/es_COLNER_uncleaned/resolve/main/meta.json
- Command line
-
hf download hf://bsoviedo/es_COLNER_uncleaned/meta.json
-
curl -L -o meta.json https://huggingface.co/bsoviedo/es_COLNER_uncleaned/resolve/main/meta.json
890 Bytes
| { | |
| "lang":"es", | |
| "name":"COLNER_uncleaned", | |
| "version":"0.0.0", | |
| "description":"", | |
| "author":"", | |
| "email":"", | |
| "url":"", | |
| "license":"", | |
| "spacy_version":">=3.8.4,<3.9.0", | |
| "spacy_git_version":"85cc763", | |
| "vectors":{ | |
| "width":0, | |
| "vectors":0, | |
| "keys":0, | |
| "name":null | |
| }, | |
| "labels":{ | |
| "transformer":[ | |
| ], | |
| "ner":[ | |
| "LOC" | |
| ] | |
| }, | |
| "pipeline":[ | |
| "transformer", | |
| "ner" | |
| ], | |
| "components":[ | |
| "transformer", | |
| "ner" | |
| ], | |
| "disabled":[ | |
| ], | |
| "performance":{ | |
| "ents_f":0.9677419355, | |
| "ents_p":0.960591133, | |
| "ents_r":0.975, | |
| "ents_per_type":{ | |
| "LOC":{ | |
| "p":0.960591133, | |
| "r":0.975, | |
| "f":0.9677419355 | |
| } | |
| }, | |
| "transformer_loss":20.5158823526, | |
| "ner_loss":88.1929156102 | |
| }, | |
| "requirements":[ | |
| "spacy-transformers>=1.3.5,<1.4.0" | |
| ] | |
| } |