Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:10044
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use lingtrain/labse-sakha-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lingtrain/labse-sakha-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lingtrain/labse-sakha-2") sentences = [ "Бу улахан уол туох идэлээх буолуом этэ дии саныыгын? — диир.", "Тут проходит поперек свежая тропинка: авось кто-нибудь найдет его, если боги Тангха Джылга. предопределили ему жизнь!»", "Вот ты, старший сын, как думаешь, в чем твое призвание? — спрашивает.", "Опять удача улыбается им: добывают лису, волка, лося." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from lingtrain/labse-sakha-2: direct link, hf CLI and curl.
- Browser
- Download file 13.6 MB
-
https://huggingface.co/lingtrain/labse-sakha-2/resolve/main/tokenizer.json
- Command line
-
hf download hf://lingtrain/labse-sakha-2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/lingtrain/labse-sakha-2/resolve/main/tokenizer.json
13.6 MB
- Xet hash:
- 9061164c657de59e1ffb961b1afb95a5e0afab558522095c235bb448a22b7c74
- Size of remote file:
- 13.6 MB
- SHA256:
- 92262b29204f8fdc169a63f9005a0e311a16262cef4d96ecfe2a7ed638662ed3
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