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
Subida inicial LexiMus-BETO-per-v1 [15/25]
Browse files- parser/cfg +13 -0
parser/cfg
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{
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"moves":null,
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"update_with_oracle_cut_size":100,
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"multitasks":[
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],
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"min_action_freq":30,
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"learn_tokens":false,
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"beam_width":1,
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"beam_density":0.0,
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"beam_update_prob":0.0,
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"incorrect_spans_key":null
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}
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