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 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lingtrain/labse-chuvash with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lingtrain/labse-chuvash") 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
| [ | |
| { | |
| "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" | |
| } | |
| ] |