rubert-base-cased-sentiment-new

Model Card for RuBERT for Sentiment Analysis

Model Details

Model Description

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.

Downstream Use [Optional]

More information needed.

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

Training Procedure

Preprocessing

More information needed

Speeds, Sizes, Times

More information needed

Evaluation

Testing Data, Factors & Metrics

Testing Data

More information needed

Factors

More information needed

Metrics

More information needed

Results

More information needed

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

Technical Specifications [optional]

Model Architecture and Objective

More information needed

Compute Infrastructure

More information needed

Hardware

More information needed

Software

More information needed.

Citation

More information needed.

Glossary [optional]

More information needed

More Information [optional]

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

How to Get Started with the Model

Use the code below to get started with the model.

<details> <summary> Click to expand </summary>

Needed pytorch trained model presented in Drive.

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

</details>