readme: ---
  language:
  - ru
  
  tags:
  - sentiment
  - text-classification
  
  datasets:
  - Tatyana/ru_sentiment_dataset
  ---
  
  
  # 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](https://github.com/T-Sh/Sentiment-Analysis)
   	
  
  
  # 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)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). 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](https://huggingface.co/datasets/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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
   
  - **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](https://drive.google.com/drive/folders/1EnJBq0dGfpjPxbVjybqaS7PsMaPHLUIl?usp=sharing).
  
  Load and place model.pth.tar in folder next to another files of a model.
  
  ```python
   
  !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>
  
  