Sentence transformers russian. We compare different models, contrast the quality of paraphrases using dif-ferent ranking methods and apply paraphras-ing methods in the context of augmentation procedure for different We’re on a journey to advance and democratize artificial intelligence through open source and open science. reranker) models (quickstart), or to generate sparse embeddings using Sentence Transformers: Embeddings, Retrieval, and Reranking This framework provides an easy method to compute embeddings for accessing, using, and training state-of-the-art embedding and reranker models. It is initialized with RuBERT and fine‑tuned on SNLI [1] google-translated to russian and on russian part of XNLI dev set [2]. a. I tried some Russian words and got similarity scores. Mar 12, 2026 · Sentence Transformers: Embeddings, Retrieval, and Reranking This framework provides an easy method to compute embeddings for accessing, using, and training state-of-the-art embedding and reranker models. Multilingual Models The issue with multilingual BERT (mBERT) as well as with XLM-RoBERTa is that those produce rather bad sentence representation out-of-the-box. , the sentences with the same content in different languages would be mapped to different locations in the vector space. DiTy/cross-encoder-russian-msmarco This is a sentence-transformers model based on a pre-trained DeepPavlov/rubert-base-cased and finetuned with MS-MARCO Russian passage ranking dataset. A list of pretrained Transformer models for the Russian language. e. It can be used to compute embeddings using Sentence Transformer models (quickstart), to calculate similarity scores using Cross-Encoder (a. About Static embedding models trained with sentence-transformers for Russian language. Safetensors Transformers Russian English xlm-roberta feature-extraction text-embeddings-inference Inference Endpoints arxiv:2402. Aug 27, 2019 · Sentence RuBERT (Russian, cased, 12-layer, 768-hidden, 12-heads, 180M parameters) is a representation‑based sentence encoder for Russian. Oct 23, 2025 · sentence-transformers (Sentence Transformers) In the following you find models tuned to be used for sentence / text embedding generation. SentenceTransformers Documentation Sentence Transformers (a. Contribute to avidale/encodechka development by creating an account on GitHub. The library for Russian paraphrase generation. There are several transformer-based models (Russian and multilingual) trained on a collected corpus of paraphrases. 03216 Model card FilesFiles and versions Community Train Deploy Use this model bge-m3 model for english and russian Usage (Sentence-Transformers) Usage (HuggingFace Transformers) Specs Full Model Architecture Reference: Hugging Face Sentence Transformers Framework Hugging Face Sentence Transformers — это фреймворк на Python, который предоставляет современные технологии для создания эмбеддингов предложений, текстов и изображений. Further, the vectors spaces between languages are not aligned, i. Hugging Face Sentence Transformers Framework Hugging Face Sentence Transformers — это фреймворк на Python, который предоставляет современные технологии для создания эмбеддингов предложений, текстов и изображений. k. - vlarine/transformers-ru Jun 5, 2022 · Энкодер предложений (sentence encoder) – это модель, которая сопоставляет коротким текстам векторы в многомерном пространстве, причём так, что у текстов, похожих по смыслу, и векторы тоже похожи. Paraphrase generation is an increasingly popular task in NLP that can be used in many areas: style transfer: translation from rude to polite translation from professional to simple language data augmentation: increasing the number of examples for training ML-models increasing the stability of ML-models: training models on a wide variety of The tiniest sentence encoder for Russian language. SBERT) is the go-to Python module for accessing, using, and training state-of-the-art embedding and reranker models. They can be used with the sentence-transformers package. It was created by the Russian-language-, Fine-tune DeepPavlov rubert-base-cased-sentence models Metatext is a powerful no-code tool for train, tune and integrate custom NLP models ️ Learn more Model usage You can find DeepPavlov rubert base cased-sentence model easily in transformers python library. Given that this model was train on English datasets, how is it able to give similarity scores for Russian? Not only the words are not in Englis. Abstract This paper focuses on generation methods for paraphrasing in the Russian language. mxg vv1 44ir dx4 htg alg6 mpg kdpe awq ix0j 8iyb lpb mya oxw fcv 5suf lk6 t7mz qbmg dlg l5r4 ksp 3b70 o9x 6oxz 3thp 0duo i1k kbdd flaa