library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- mteb
- sentence-transformers
- feature-extraction
- sentence-similarity
license: mit
language:
- multilingual
- en
- sr
djovak/embedic-small
Say hello to Embedić, a group of new text embedding models finetuned for the Serbian language!
These models are particularly useful in Information Retrieval and RAG purposes. Check out images showcasing benchmark performance, you can beat previous SOTA with 5x fewer parameters!
Although specialized for Serbian(Cyrillic and Latin scripts), Embedić is Cross-lingual(it understands English too). So you can embed English docs, Serbian docs, or a combination of the two :)
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["ko je Nikola Tesla?", "Nikola Tesla je poznati pronalazač", "Nikola Jokić je poznati košarkaš"]
model = SentenceTransformer('djovak/embedic-small')
embeddings = model.encode(sentences)
print(embeddings)
Important usage notes
- "ošišana ćirilica" (usage of c instead of ć, etc...) significantly deacreases search quality
- The usage of uppercase letters for named entities can significantly improve search quality
Evaluation
Model description:
| Model Name | Dimension | Sequence Length | Parameters |
|---|---|---|---|
| intfloat/multilingual-e5-small | 384 | 512 | 117M |
| djovak/embedic-small | 384 | 512 | 117M |
| intfloat/multilingual-e5-base | 768 | 512 | 278M |
| djovak/embedic-base | 768 | 512 | 278M |
| intfloat/multilingual-e5-large | 1024 | 512 | 560M |
| djovak/embedic-large | 1024 | 512 | 560M |
BM25-ENG - Elasticsearch with English analyzer
BM25-SRB - Elasticsearch with Serbian analyzer
evaluation resultsresults
Evaluation on 3 tasks: Information Retrieval, Sentence Similarity, and Bitext mining. I personally translated the STS17 cross-lingual evaluation dataset and Spent 6,000$ on Google translate API, translating 4 IR evaluation datasets into Serbian language.
Evaluation datasets will be published as Part of MTEB benchmark in the near future.
Contact
If you have any question or sugestion related to this project, you can open an issue or pull request. You can also email me at [email protected]
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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()
)
License
Embedić models are licensed under the MIT License. The released models can be used for commercial purposes free of charge.