az-en-MiniLM-L6

This is a sentence-transformer model that maps sentences & paragraphs in Azerbaijani (az) and English (en) to a 384-dimensional dense vector space. It is designed for tasks like semantic textual similarity, semantic search, paraphrase mining, text classification, and clustering for these two languages.

The model is based on sentence-transformers/all-MiniLM-L6-v2 and was fine-tuned using knowledge distillation from the high-performance BAAI/bge-small-en-v1.5 English embedding model. A custom bilingual (Azerbaijani-English) SentencePiece Unigram tokenizer with a vocabulary of ~50k was trained from scratch and is used by this model.

Model Details

• Base Architecture: sentence-transformers/all-MiniLM-L6-v2 (6 layers, 384 hidden dimension, 12 attention heads)
• Parameters: ~30.2 Million (after vocabulary expansion)
• Tokenizer: Custom bilingual (AZ-EN) SentencePiece Unigram, vocab size ~50k. Available at LocalDoc/az-en-unigram-tokenizer-50k. You can get train code from this repository https://github.com/vrashad/azerbaijani_tokenizer
• Output Dimension: 384
• Max Sequence Length: 512 tokens
• Training: Fine-tuned for 3 epochs on a parallel corpus of ~4.14 million Azerbaijani-English sentence pairs using MSELoss for knowledge distillation from BAAI/bge-small-en-v1.5.

Performance on Azerbaijani STS Benchmarks

This model demonstrates strong performance on Azerbaijani Semantic Textual Similarity (STS) tasks LocalDoc-Azerbaijan/STS-Benchmark, achieving results competitive with, and in some cases surpassing, larger multilingual models.

The following results were obtained after 3 epochs of training :

Comparison with other models on (assumed) Azerbaijani STS Benchmarks (Average Pearson):

• LocalDoc/TEmA-small: 0.7959
• Cohere/embed-multilingual-v3.0: 0.7823
• BAAI/bge-m3: 0.7577
• intfloat/multilingual-e5-large-instruct: 0.7377
• Cohere/embed-multilingual-v2.0: 0.7318
• intfloat/multilingual-e5-large: 0.7280
• OpenAI/text-embedding-3-large: 0.7288
• LocalDoc/az-en-MiniLM-L6-v2: 0.7266
• sentence-transformers/LaBSE: 0.7250
• intfloat/multilingual-e5-small: 0.7242
• Cohere/embed-multilingual-light-v3.0: 0.7142
• intfloat/multilingual-e5-base: 0.6960

How to Use

First, install the sentence-transformers library:

pip install -U sentence-transformers

from sentence_transformers import SentenceTransformer

model_id = "LocalDoc/az-en-MiniLM-L6-v2"

try:
    model = SentenceTransformer(model_id)
    print(f"Model {model_id} loaded successfully!")
except Exception as e:
    print(f"Failed to load model. Ensure the tokenizer 'LocalDoc/az-en-unigram-tokenizer-50k' is accessible and its dependencies (protobuf, sentencepiece_model_pb2.py) are met if loading fails.")
    print(f"Error: {e}")
    # You might need to ensure the tokenizer can be loaded.
    # If the tokenizer requires it (it shouldn't if it's correctly packaged on the Hub by your tokenizer repo):
    # !pip install protobuf
    # !wget -P ./az_en_tokenizer_hf/ https://raw.githubusercontent.com/google/sentencepiece/master/python/src/sentencepiece/sentencepiece_model_pb2.py
    # model = SentenceTransformer(model_id)

# Example Azerbaijani sentences
sentences_az = [
    "Azərbaycanın paytaxtı Bakı şəhəridir.",
    "Bu gün hava çox istidir."
]

# Example English sentences
sentences_en = [
    "The capital of Azerbaijan is the city of Baku.",
    "The weather is very hot today.",
    "I enjoy reading books."
]

print("\nEncoding Azerbaijani sentences...")
embeddings_az = model.encode(sentences_az)
for sent, emb in zip(sentences_az, embeddings_az):
    print(f"Sentence: {sent}")
    print(f"Embedding shape: {emb.shape}, first 3 dims: {emb[:3]}\n")

print("Encoding English sentences...")
embeddings_en = model.encode(sentences_en)
for sent, emb in zip(sentences_en, embeddings_en):
    print(f"Sentence: {sent}")
    print(f"Embedding shape: {emb.shape}, first 3 dims: {emb[:3]}\n")

Example of calculating similarity

from sentence_transformers.util import cos_sim

similarity_matrix = cos_sim(embeddings_az[0], embeddings_en[0])
print(f"Similarity between '{sentences_az[0]}' and '{sentences_en[0]}': {similarity_matrix.item():.4f}")

similarity_matrix_diff = cos_sim(embeddings_az[0], embeddings_en[2])
print(f"Similarity between '{sentences_az[0]}' and '{sentences_en[2]}': {similarity_matrix_diff.item():.4f}")

Training

This model was fine-tuned from sentence-transformers/all-MiniLM-L6-v2 using a knowledge distillation setup.

• Teacher Model: BAAI/bge-small-en-v1.5 (used to generate target embeddings for English sentences).
• Student Model: Initialized from sentence-transformers/all-MiniLM-L6-v2.
• Tokenizer: A custom bilingual (Azerbaijani-English) SentencePiece Unigram tokenizer (LocalDoc/az-en-unigram-tokenizer-50k) was used. The student model's token embedding layer was resized to match the new vocabulary size (~50k).
• Training Data: A parallel corpus of approximately 4.14 million Azerbaijani-English sentence pairs.
• Loss Function: MSELoss — the student model was trained to produce embeddings for both Azerbaijani and English sentences that are similar to the teacher model's