This is a truncated version of sentence-transformers/LaBSE, which is, in turn, a port of LaBSE by Google. The current model has only English and Russian tokens left in the vocabulary. Thus, the vocabulary is 10% of the original, and number of parameters in the whole model is 27% of the original, without any loss in the quality of English and Russian embeddings. The model has been truncated in this notebook. You can adapt it for other languages (like EIStakovskii/LaBSE-fr-de), models or datasets.
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This is the DeepPavlov/rubert-base-cased fine-tuned to predict the logical relationship between two short texts: entailment, contradiction, or neutral. You can also use this model for zero-shot short text classification (by labels only), e.g. for sentiment analysis: The model has been trained on a series of NLI datasets automatically translated to Russian from English.
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This is a very small distilled version of the bert-base-multilingual-cased model for Russian and English (45 MB, 12M parameters). There is also an **updated version of this model**, rubert-tiny2, with a larger vocabulary and better quality on practically all Russian NLU tasks. This model is useful if you want to fine-tune it for a relatively simple Russian task (e.g. NER or sentiment classification), and you care more about speed and size than about accuracy. It is approximately x10 smaller and faster than a base-sized BERT. Its `[CLS]` embeddings can be used as a sentence representation aligned between Russian and English. It was trained on the Yandex Translate corpus, OPUS-100 and Tatoeba, using MLM loss (distilled from bert-base-multilingual-cased), translation ranking loss, and `[CLS]` embeddings distilled from LaBSE, rubert-base-cased-sentence, Laser and USE.
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This is the cointegrated/rubert-tiny model fine-tuned for classification of sentiment for short Russian texts. The problem is formulated as multiclass classification: `negative` vs `neutral` vs `positive`. We trained the model on the datasets collected by Smetanin. We have converted all training data into a 3-class format and have up- and downsampled the training data to balance both the sources and the classes. The training code is available as a Colab notebook. The metrics on the balanced test set are the following:
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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. The function below estimates the probability that the text is either toxic OR dangerous:
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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: - a larger vocabulary: 83828 tokens instead of 29564; - larger supported sequences: 2048 instead of 512; - 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.
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This is the cointegrated/rubert-tiny2 model fine-tuned for classification of emotions in Russian sentences. The task is multilabel classification, because one sentence can contain multiple emotions. The model on the CEDR dataset described in the paper "Data-Driven Model for Emotion Detection in Russian Texts" by Sboev et al. The model has been trained with Adam optimizer for 40 epochs with learning rate `1e-5` and batch size 64 in this notebook.
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This is a port of the multilingual SONAR text encoder (https://huggingface.co/facebook/SONAR) to the `transformers` format from `fairseq2`. Its embeddings are expected be equal to those the official implementation (https://github.com/facebookresearch/SONAR), but the latter stays the source of truth. The encoder supports the same 202 languages as NLLB-200 (see also the source model card and FLORES-200 lang code mapping).
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