# Some Like It Small: Czech Semantic Embedding Models for Industry Applications

arXiv: https://arxiv.org/abs/2311.13921 · HTML: https://mlsanity.com/paper/2311.13921/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 39 references that are in the map.
- First posted on arXiv 23 Nov 2023.

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://mlsanity.com/paper/2004.12832/) (2020)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2010.08191/) (2020)
- [SentEval: An Evaluation Toolkit for Universal Sentence Representations](https://mlsanity.com/paper/1803.05449/) (2018)
- [Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval](https://mlsanity.com/paper/2108.05540/) (2021)
- [Condenser: a Pre-training Architecture for Dense Retrieval](https://mlsanity.com/paper/2104.08253/) (2021)
- [Dense Text Retrieval based on Pretrained Language Models: A Survey](https://mlsanity.com/paper/2211.14876/) (2022)
- [DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings](https://mlsanity.com/paper/2204.10298/) (2022)
- [RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder](https://mlsanity.com/paper/2205.12035/) (2022)
- [TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning](https://mlsanity.com/paper/2104.06979/) (2021)
- [SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval](https://mlsanity.com/paper/2207.02578/) (2022)
- [RobeCzech: Czech RoBERTa, a monolingual contextualized language representation model](https://mlsanity.com/paper/2105.11314/) (2021)
- [InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2210.06432/) (2022)
- [RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank](https://mlsanity.com/paper/2305.16726/) (2023)
- [Czert -- Czech BERT-like Model for Language Representation](https://mlsanity.com/paper/2103.13031/) (2021)
- [Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction](https://mlsanity.com/paper/2203.13088/) (2022)
- [Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations](https://mlsanity.com/paper/2109.13059/) (2021)
- [Announcing CzEng 2.0 Parallel Corpus with over 2 Gigawords](https://mlsanity.com/paper/2007.03006/) (2020)
- [Comparison of Czech Transformers on Text Classification Tasks](https://mlsanity.com/paper/2107.10042/) (2021)
- [Siamese BERT-based Model for Web Search Relevance Ranking Evaluated on a New Czech Dataset](https://mlsanity.com/paper/2112.01810/) (2021)
- [Czech Text Document Corpus v 2.0](https://mlsanity.com/paper/1710.02365/) (2017)

## Built on by (most cited first, 3 of 3)

- [CWRCzech: 100M Query-Document Czech Click Dataset and Its Application to Web Relevance Ranking](https://mlsanity.com/paper/2405.20994/) (2024)
- [A Comparative Study of Text Retrieval Models on DaReCzech](https://mlsanity.com/paper/2411.12921/) (2024)
- [Intrinsic vs. Extrinsic Evaluation of Czech Sentence Embeddings: Semantic Relevance Doesn't Help with MT Evaluation](https://mlsanity.com/paper/2506.20203/) (2025)

## Abstract (arXiv)

This article focuses on the development and evaluation of Small-sized Czech sentence embedding models. Small models are important components for real-time industry applications in resource-constrained environments. Given the limited availability of labeled Czech data, alternative approaches, including pre-training, knowledge distillation, and unsupervised contrastive fine-tuning, are investigated. Comprehensive intrinsic and extrinsic analyses are conducted, showcasing the competitive performance of our models compared to significantly larger counterparts, with approximately 8 times smaller size and 5 times faster speed than conventional Base-sized models. To promote cooperation and reproducibility, both the models and the evaluation pipeline are made publicly accessible. Ultimately, this article presents practical applications of the developed sentence embedding models in Seznam.cz, the Czech search engine. These models have effectively replaced previous counterparts, enhancing the overall search experience for instance, in organic search, featured snippets, and image search. This transition has yielded improved performance.