Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1000000
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use lingtrain/labse-chuvash-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lingtrain/labse-chuvash-2 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lingtrain/labse-chuvash-2") sentences = [ "Акӑ ӗнтӗ Чакак кимӗ ҫине сикрӗ, Коля пӗр-икӗ хут шнуртан туртрӗ те, мотор кӗрлесе те кайрӗ, унтан кимӗ утрав еннелле вӗҫтерчӗ.", "Вот Сорока вскочил в лодку, Коля дернул за шнур, раз, другой, мотор затрещал, и лодка понеслась к острову.", "Победа римского флота в гавани Эвносте.", "Повесть Бориса Горбатова о подвиге и героизме советских людей во время Великой Отечественной войны." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download sentence_bert_config.json from lingtrain/labse-chuvash-2: direct link, hf CLI and curl.
- Browser
- Download file 53 Bytes
-
https://huggingface.co/lingtrain/labse-chuvash-2/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://lingtrain/labse-chuvash-2/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/lingtrain/labse-chuvash-2/resolve/main/sentence_bert_config.json
53 Bytes
| { | |
| "max_seq_length": 256, | |
| "do_lower_case": false | |
| } |