Instructions to use latincy/la_vectors_floret_lg with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use latincy/la_vectors_floret_lg with spaCy:
!pip install https://huggingface.co/latincy/la_vectors_floret_lg/resolve/main/la_vectors_floret_lg-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("la_vectors_floret_lg") # Importing as module. import la_vectors_floret_lg nlp = la_vectors_floret_lg.load() - Notebooks
- Google Colab
- Kaggle
metadata
tags:
- spacy
language:
- la
license: mit
Code required to train lg floret embeddings for Latin on LatinCy Assets data. Based on spaCy project Train floret vectors from Wikipedia and OSCAR.
| Feature | Description |
|---|---|
| Name | la_vectors_floret_lg |
| Version | 3.8.0 |
| spaCy | >=3.8.3,<3.9.0 |
| Default Pipeline | |
| Components | |
| Vectors | -1 keys, 200000 unique vectors (300 dimensions) |
| Sources | UD_Latin-Perseus UD_Latin-PROIEL UD_Latin-ITTB UD_Latin-LLCT UD_Latin-UDante Wikipedia OSCAR Corpus Thomisticum The Latin Library CLTK-Tesserae Latin Patrologia Latina |
| License | MIT |
| Author | Patrick J. Burns |