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metadata
license: mit
language:
  - ru
metrics:
  - f1
  - roc_auc
  - precision
  - recall
pipeline_tag: text-classification
tags:
  - sentiment-analysis
  - multi-class-classification
  - sentiment analysis
  - rubert
  - sentiment
  - bert
  - russian
  - multiclass
  - classification
datasets:
  - sismetanin/rureviews
  - RuSentiment
  - LinisCrowd2015
  - LinisCrowd2016
  - KaggleRussianNews

This is RuBERT model fine-tuned for sentiment classification of short Russian texts. The task is a multi-class classification with the following labels:

0: neutral
1: positive
2: negative

Label to Russian label:

neutral: нейтральный
positive: позитивный
negative: негативный

Usage

from transformers import pipeline
model = pipeline(model="seara/rubert-base-cased-russian-sentiment")
model("Привет, ты мне нравишься!")
# [{'label': 'positive', 'score': 0.9818321466445923}]

Dataset

This model was trained on the union of the following datasets:

  • Kaggle Russian News Dataset
  • Linis Crowd 2015
  • Linis Crowd 2016
  • RuReviews
  • RuSentiment

An overview of the training data can be found on S. Smetanin Github repository.

Download links for all Russian sentiment datasets collected by Smetanin can be found in this repository.

Training

Training were done in this project with this parameters:

tokenizer.max_length: 256
batch_size: 32
optimizer: adam
lr: 0.00001
weight_decay: 0
epochs: 2

Train/validation/test splits are 80%/10%/10%.

Eval results (on test split)

neutral positive negative macro avg weighted avg
precision 0.72 0.85 0.75 0.77 0.77
recall 0.75 0.84 0.72 0.77 0.77
f1-score 0.73 0.84 0.73 0.77 0.77
auc-roc 0.86 0.96 0.92 0.91 0.91
support 5196 3831 3599 12626 12626