Rubert Base Cased Sentiment New

🚀 Model Card for RuBERT for Sentiment Analysis

This model is designed for sentiment analysis of Russian texts, providing a solution for text classification tasks.

🚀 Quick Start

Use the code below to get started with the model.

!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"])

✨ Features

• Sentiment Classification: Capable of classifying the sentiment of Russian texts.
• Based on BERT: Built upon the powerful BERT architecture.

📦 Installation

The installation steps are included in the quick start code snippet above.

📚 Documentation

Model Details

• Model Description: This model is for Russian texts sentiment classification.
  • Developed by: Tatyana Voloshina
  • Shared by [Optional]: Tatyana Voloshina
  • Model type: Text Classification
  • Language(s) (NLP): More information needed
  • License: More information needed
  • Parent Model: BERT
  • Resources for more information:
    • GitHub Repo

Uses

Direct Use

This model can be used for the task of text classification.

Out-of-Scope Use

The model should not be used to intentionally create hostile or alienating environments for people.

Bias, Risks, and Limitations

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Training Details

Training Data

Model trained on Tatyana/ru_sentiment_dataset

Model Examination

Labels meaning

• 0: NEUTRAL
• 1: POSITIVE
• 2: NEGATIVE

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: More information needed
• Hours used: More information needed
• Cloud Provider: More information needed
• Compute Region: More information needed
• Carbon Emitted: More information needed

Model Card Authors [optional]

Tatyana Voloshina in collaboration with Ezi Ozoani and the Hugging Face team

Model Card Contact

More information needed

🔧 Technical Details

⚠️ Important Note

The model may generate predictions that include disturbing and harmful stereotypes. Users should be aware of the risks, biases and limitations of the model.

💡 Usage Tip

Refer to the quick start section to start using the model.