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--- |
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language: |
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- ru |
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tags: |
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- sentiment |
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- text-classification |
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--- |
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# RuBERT for Sentiment Analysis |
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Short Russian texts sentiment classification |
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This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on aggregated corpus of 351.797 texts. |
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## Labels |
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0: NEUTRAL |
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1: POSITIVE |
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2: NEGATIVE |
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## How to use |
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```python |
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import torch |
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from transformers import AutoModelForSequenceClassification |
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from transformers import BertTokenizerFast |
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tokenizer = BertTokenizerFast.from_pretrained('blanchefort/rubert-base-cased-sentiment') |
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model = AutoModelForSequenceClassification.from_pretrained('blanchefort/rubert-base-cased-sentiment', return_dict=True) |
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@torch.no_grad() |
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def predict(text): |
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inputs = tokenizer(text, max_length=512, padding=True, truncation=True, return_tensors='pt') |
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outputs = model(**inputs) |
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predicted = torch.nn.functional.softmax(outputs.logits, dim=1) |
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predicted = torch.argmax(predicted, dim=1).numpy() |
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return predicted |
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``` |
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## Datasets used for model training |
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**[RuTweetCorp](https://study.mokoron.com/)** |
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> Рубцова Ю. Автоматическое построение и анализ корпуса коротких текстов (постов микроблогов) для задачи разработки и тренировки тонового классификатора //Инженерия знаний и технологии семантического веба. – 2012. – Т. 1. – С. 109-116. |
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**[RuReviews](https://github.com/sismetanin/rureviews)** |
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> RuReviews: An Automatically Annotated Sentiment Analysis Dataset for Product Reviews in Russian. |
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**[RuSentiment](http://text-machine.cs.uml.edu/projects/rusentiment/)** |
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> A. Rogers A. Romanov A. Rumshisky S. Volkova M. Gronas A. Gribov RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian. Proceedings of COLING 2018. |
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**[Отзывы о медучреждениях](https://github.com/blanchefort/datasets/tree/master/medical_comments)** |
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> Датасет содержит пользовательские отзывы о медицинских учреждениях. Датасет собран в мае 2019 года с сайта prodoctorov.ru |
