Sbert Ru Sentiment Rusentiment

🚀 SBERT-Large-Base-ru-sentiment-RuSentiment

SBERT-Large-ru-sentiment-RuSentiment is a fine - tuned model based on SBERT-Large, which uses the RuSentiment dataset of general - domain Russian - language posts from the largest Russian social network, VKontakte, for sentiment analysis.

🚀 Quick Start

This model is ready for use after fine - tuning. You can load it through relevant machine - learning frameworks and apply it to Russian sentiment analysis tasks.

✨ Features

• Fine - tuned on Russian data: It uses the RuSentiment dataset, which is very suitable for sentiment analysis of Russian social media texts.
• High performance: As shown in the comparison table, it has good performance on multiple Russian sentiment analysis datasets.

📚 Documentation

Model Performance Comparison

The table shows per - task scores and a macro - average of those scores to determine a model’s position on the leaderboard. For datasets with multiple evaluation metrics (e.g., macro F1 and weighted F1 for RuSentiment), we use an unweighted average of the metrics as the score for the task when computing the overall macro - average. The same strategy for comparing models’ results was applied in the GLUE benchmark.

📄 License

The license information is not provided in the original document.

Citation

If you find this repository helpful, feel free to cite our publication:

@article{Smetanin2021Deep,
  author = {Sergey Smetanin and Mikhail Komarov},
  title = {Deep transfer learning baselines for sentiment analysis in Russian},
  journal = {Information Processing & Management},
  volume = {58},
  number = {3},
  pages = {102484},
  year = {2021},
  issn = {0306-4573},
  doi = {0.1016/j.ipm.2020.102484}
}

Dataset:

@inproceedings{rogers2018rusentiment,
  title={RuSentiment: An enriched sentiment analysis dataset for social media in Russian},
  author={Rogers, Anna and Romanov, Alexey and Rumshisky, Anna and Volkova, Svitlana and Gronas, Mikhail and Gribov, Alex},
  booktitle={Proceedings of the 27th international conference on computational linguistics},
  pages={755--763},
  year={2018}
}