David Dale

Model · Text classification

rubert-tiny-toxicity

David Dale

This is the cointegrated/rubert-tiny model fine-tuned for classification of toxicity and inappropriateness for short informal Russian texts, such as comments in social networks. The problem is formulated as multilabel classification with the following classes: - non-toxic: the text does NOT contain insults, obscenities, and threats, in the sense of the OK ML Cup competition. - insult - obscenity - threat - dangerous: the text is inappropriate, in the sense of Babakov et.al., i.e. it can harm the reputation of the speaker. A text can be considered safe if it is BOTH non-toxic and NOT dangerous. The function below estimates the probability that the text is either toxic OR dangerous: The model…

Model · Sentence similarity

rubert-tiny2

David Dale

This is an updated version of cointegrated/rubert-tiny: a small Russian BERT-based encoder with high-quality sentence embeddings. This post in Russian gives more details. The differences from the previous version include: - sentence embeddings approximate LaBSE closer than before; - meaningful segment embeddings (tuned on the NLI task) - the model is focused only on Russian. The model should be used as is to produce sentence embeddings (e.g. for KNN classification of short texts) or fine-tuned for a downstream task. Sentence embeddings can be produced as follows: Alternatively, you can use the model with sentencetransformers: For those who want to run the inference with VLLM, there is a…

Model · Zero-shot classification

rubert-base-cased-nli-threeway

David Dale

This is the DeepPavlov/rubert-base-cased fine-tuned to predict the logical relationship between two short texts: entailment, contradiction, or neutral. How to run the model for NLI: You can also use this model for zero-shot short text classification (by labels only), e.g. for sentiment analysis: Alternatively, you can use Huggingface pipelines for inference. The model has been trained on a series of NLI datasets automatically translated to Russian from English. Most datasets were taken from the repo of Felipe Salvatore: Some datasets obtained from the original sources: The table below shows ROC AUC (one class vs rest) for five models on the corresponding dev sets: - tiny: a small BERT…