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RuBERT for Sentiment Analysis

Russian texts sentiment classification.

Model trained on Tatyana/ru_sentiment_dataset

Labels meaning

0: NEUTRAL
1: POSITIVE
2: NEGATIVE

How to use


!pip install tensorflow-gpu
!pip install deeppavlov
!python -m deeppavlov install squad_bert
!pip install fasttext
!pip install transformers
!python -m deeppavlov install bert_sentence_embedder

from deeppavlov import build_model

model = build_model(path_to_model/rubert_sentiment.json)
model(["Сегодня хорошая погода", "Я счастлив проводить с тобою время", "Мне нравится эта музыкальная композиция"])

Needed pytorch trained model presented in Drive.

Load and place model.pth.tar in folder next to another files of a model.

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Dataset used to train Tatyana/rubert-base-cased-sentiment-new