18 models match the criteria

RuBERT is a Russian BERT model trained on Russian Wikipedia and news data, with 180 million parameters, supporting masked language modeling and next sentence prediction tasks.

Russian conversational model trained on OpenSubtitles, Dirty, Pikabu, and Taiga corpus social media sections

A Russian natural language inference model fine-tuned from DeepPavlov/rubert-base-cased, capable of predicting logical relationships (entailment/contradiction/neutral) between two texts

A sentence-transformers model based on the DeepPavlov/rubert-base-cased pre-trained model and fine-tuned on the MS-MARCO Russian passage ranking dataset, designed for Russian information retrieval tasks.

A sentiment analysis model based on DeepPavlov/rubert-base-cased-conversational architecture, trained on the RuSentiment dataset, capable of identifying neutral, positive, and negative emotions in Russian text.

A sentence-transformers model fine-tuned on the MS-MARCO Russian passage ranking dataset, based on the DeepPavlov/rubert-base-cased pre-trained model, designed for asymmetric semantic search in Russian.

A Russian short-text sentiment classification model based on the RuBERT architecture, supporting neutral, positive, and negative sentiment classification

Representation-based Russian sentence encoder that obtains sentence representations by averaging token embedding vectors

Russian text sentiment classification model based on BERT architecture

Russian toxicity text classifier based on rubert-base-cased-conversational model

A Russian multi-class sentiment classification model fine-tuned based on RuBERT, supporting neutral, positive, and negative sentiment classifications.

This model is a fine-tuned RuBERT pre-trained model on a medical review corpus, used to analyze the sentiment tendencies of medical reviews.

Russian sentence semantic similarity evaluation model based on ruBERT-conversational, trained on multiple paraphrase detection datasets

A text purification labeling model based on rubert-base-cased-conversational, supporting four processing types: retain, replace, delete, and insert labels.

A fine-tuned Russian BERT model based on DeepPavlov/rubert-base-cased, designed to predict logical relationships (entailment or non-entailment) between two short texts.

A sentiment analysis model based on DeepPavlov/rubert-base-cased-conversational architecture, trained on the RuReviews dataset for sentiment classification of Russian product reviews.

This is a paraphrase detector developed based on DeepPavlov, migrated to the Transformers format, used to detect whether Russian texts are paraphrases.

An extractive summarization model based on rubert-base-cased, specifically optimized for Russian news texts