Instructions to use lingtrain/labse-buryat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lingtrain/labse-buryat with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lingtrain/labse-buryat") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 97ddac1c343f7f7b65bf6ab81c1415a9f3aed85b2304c924254347bac2ee27f6
- Size of remote file:
- 1.88 GB
- SHA256:
- c47133aff8553e946b03921b4076e4d3ce587d6042724987ef78b271f0bae166
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.