sultanbi/e5-base-kazakh

This is a sentence-transformers model finetuned from intfloat/multilingual-e5-base on a translated Kazakh version of the SNLI dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: intfloat/multilingual-e5-base
• Maximum Sequence Length: 128 tokens
• Output Dimensionality: 768 dimensions
• Similarity Function: Cosine Similarity

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sultanbi/e5-base-kazakh")
# Run inference
sentences = [
    'query: Ақ көйлек киген аққұба әйел гүлді ағашта ақ құсты ұстап отыр.',
    'query: Балалар күлімсіреп, камераға қол бұлғап тұр.',
    'query: Үш бала көл жағасында тұр.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Evaluation and comparison against multilingual-e5-base in kazakh tasks

Task / Dataset	Metric	multilingual-e5-base	e5-base-kazakh	Δ (absolute)	Description
Kazakh SNLI (validation set) retrieval	R@1	0.433	0.887	+0.454	Monolingual semantic retrieval on translated SNLI (validation set)
	R@5	0.619	0.984	+0.365
	R@10	0.710	0.994	+0.284
STS-B (val, Kazakh)	Pearson	0.708	0.817	+0.109	Semantic similarity correlation (machine-translated STS-B)
	Spearman	0.721	0.825	+0.104
Triplet Accuracy	—	0.802	0.941	+0.139	Monolingual entailment discrimination

Framework Versions

• Python: 3.11.13
• Sentence Transformers: 4.1.0
• Transformers: 4.52.4
• PyTorch: 2.6.0+cu124
• Accelerate: 1.8.1
• Datasets: 3.6.0
• Tokenizers: 0.21.2

Citation

BibTeX

@article{wang2024multilingual,
  title={Multilingual E5 Text Embeddings: A Technical Report},
  author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
  journal={arXiv preprint arXiv:2402.05672},
  year={2024}
}

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}