Token Classification
spaCy
Spanish
named-entity-recognition
spanish
music
digital-humanities
historical-text
bert
Eval Results (legacy)
Instructions to use LexiMusUSAL/LexiMus-BETO-per-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use LexiMusUSAL/LexiMus-BETO-per-v1 with spaCy:
!pip install https://huggingface.co/LexiMusUSAL/LexiMus-BETO-per-v1/resolve/main/LexiMus-BETO-per-v1-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("LexiMus-BETO-per-v1") # Importing as module. import LexiMus-BETO-per-v1 nlp = LexiMus-BETO-per-v1.load() - Notebooks
- Google Colab
- Kaggle
| { | |
| "moves":null, | |
| "update_with_oracle_cut_size":100, | |
| "multitasks":[ | |
| ], | |
| "min_action_freq":30, | |
| "learn_tokens":false, | |
| "beam_width":1, | |
| "beam_density":0.0, | |
| "beam_update_prob":0.0, | |
| "incorrect_spans_key":null | |
| } |