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 modules.json from lingtrain/labse-sakha-2: direct link, hf CLI and curl.
- Browser
- Download file 461 Bytes
-
https://huggingface.co/lingtrain/labse-sakha-2/resolve/main/modules.json
- Command line
-
hf download hf://lingtrain/labse-sakha-2/modules.json
-
curl -L -o modules.json https://huggingface.co/lingtrain/labse-sakha-2/resolve/main/modules.json
461 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Dense", | |
| "type": "sentence_transformers.models.Dense" | |
| }, | |
| { | |
| "idx": 3, | |
| "name": "3", | |
| "path": "3_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |