metadata
license: apache-2.0
language:
- en
- az
base_model:
- sentence-transformers/LaBSE
pipeline_tag: sentence-similarity
Small LaBSE for English-Azerbaijani
This is an optimized version of LaBSE (Language-agnostic BERT Sentence Embeddings) specifically for English and Azerbaijani language.
Benchmark
| STSBenchmark | biosses-sts | sickr-sts | sts12-sts | sts13-sts | sts15-sts | sts16-sts | Average Pearson | Model |
|--------------|-------------|-----------|-----------|-----------|-----------|-----------|-----------------|--------------------------------------|
| 0.7363 | 0.8148 | 0.7067 | 0.7050 | 0.6535 | 0.7514 | 0.7070 | 0.7250 | sentence-transformers/LaBSE |
| 0.7400 | 0.8216 | 0.6946 | 0.7098 | 0.6781 | 0.7637 | 0.7222 | 0.7329 | LocalDoc/LaBSE-small-AZ |
| 0.5830 | 0.2486 | 0.5921 | 0.5593 | 0.5559 | 0.5404 | 0.5289 | 0.5155 | antoinelouis/colbert-xm |
| 0.7572 | 0.8139 | 0.7328 | 0.7646 | 0.6318 | 0.7542 | 0.7092 | 0.7377 | intfloat/multilingual-e5-large-instruct |
| 0.7485 | 0.7714 | 0.7271 | 0.7170 | 0.6496 | 0.7570 | 0.7255 | 0.7280 | intfloat/multilingual-e5-large |
| 0.6960 | 0.8185 | 0.6950 | 0.6752 | 0.5899 | 0.7186 | 0.6790 | 0.6960 | intfloat/multilingual-e5-base |
| 0.7376 | 0.7917 | 0.7190 | 0.7441 | 0.6286 | 0.7461 | 0.7026 | 0.7242 | intfloat/multilingual-e5-small |
| 0.7927 | 0.6672 | 0.7758 | 0.8122 | 0.7312 | 0.7831 | 0.7416 | 0.7577 | BAAI/bge-m3 |
How to Use
from transformers import AutoTokenizer, AutoModel
import torch
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("LocalDoc/LaBSE-small-AZ")
model = AutoModel.from_pretrained("LocalDoc/LaBSE-small-AZ")
# Prepare texts
texts = [
"Hello world", # English
"Salam dünya" # Azerbaijani
]
# Tokenize and generate embeddings
encoded = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
embeddings = model(**encoded).pooler_output
# Compute similarity
similarity = torch.nn.functional.cosine_similarity(embeddings[0], embeddings[1], dim=0)