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model documentation

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  ---
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  language:
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  - ru
 
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  tags:
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  - sentiment
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  - text-classification
 
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  datasets:
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  - Tatyana/ru_sentiment_dataset
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  ---
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- # RuBERT for Sentiment Analysis
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- Russian texts sentiment classification.
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  Model trained on [Tatyana/ru_sentiment_dataset](https://huggingface.co/datasets/Tatyana/ru_sentiment_dataset)
 
 
 
 
 
 
 
 
 
 
 
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  ## Labels meaning
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  0: NEUTRAL
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  1: POSITIVE
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  2: NEGATIVE
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- ## How to use
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- ```python
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  !pip install tensorflow-gpu
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  !pip install deeppavlov
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  !python -m deeppavlov install squad_bert
@@ -32,9 +181,6 @@ from deeppavlov import build_model
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  model = build_model(path_to_model/rubert_sentiment.json)
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  model(["Сегодня хорошая погода", "Я счастлив проводить с тобою время", "Мне нравится эта музыкальная композиция"])
 
 
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- ```
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-
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- Needed pytorch trained model presented in [Drive](https://drive.google.com/drive/folders/1EnJBq0dGfpjPxbVjybqaS7PsMaPHLUIl?usp=sharing).
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-
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- Load and place model.pth.tar in folder next to another files of a model.
 
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  ---
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  language:
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  - ru
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+
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  tags:
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  - sentiment
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  - text-classification
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+
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  datasets:
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  - Tatyana/ru_sentiment_dataset
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  ---
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+ # Model Card for RuBERT for Sentiment Analysis
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+
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+ # Model Details
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+
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+ ## Model Description
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+
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+ Russian texts sentiment classification.
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+
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+ - **Developed by:** Tatyana Voloshina
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+ - **Shared by [Optional]:** Tatyana Voloshina
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+ - **Model type:** Text Classification
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+ - **Language(s) (NLP):** More information needed
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+ - **License:** More information needed
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+ - **Parent Model:** BERT
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+ - **Resources for more information:**
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+ - [GitHub Repo](https://github.com/T-Sh/Sentiment-Analysis)
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+
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+
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+
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+ # Uses
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+
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+
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+ ## Direct Use
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+ This model can be used for the task of text classification.
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+
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+ ## Downstream Use [Optional]
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+
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+ More information needed.
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+
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+ ## Out-of-Scope Use
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+
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+ The model should not be used to intentionally create hostile or alienating environments for people.
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+
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+ # Bias, Risks, and Limitations
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+
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+
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+ 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.
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+
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+
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+
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+ ## Recommendations
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+
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+
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+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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+
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+ # Training Details
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+
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+ ## Training Data
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+
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  Model trained on [Tatyana/ru_sentiment_dataset](https://huggingface.co/datasets/Tatyana/ru_sentiment_dataset)
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+
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+ ## Training Procedure
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+
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+
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+ ### Preprocessing
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+
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+ More information needed
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+
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+
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+ ### Speeds, Sizes, Times
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+ More information needed
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+
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+ # Evaluation
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+
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+
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+ ## Testing Data, Factors & Metrics
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+
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+ ### Testing Data
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+ More information needed
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+
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+
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+ ### Factors
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+ More information needed
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+
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+ ### Metrics
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+ More information needed
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+
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+ ## Results
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+
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+ More information needed
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+
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+
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+ # Model Examination
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  ## Labels meaning
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  0: NEUTRAL
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  1: POSITIVE
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  2: NEGATIVE
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+
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+ # Environmental Impact
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+
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+ 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).
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+
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+ - **Hardware Type:** More information needed
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+ - **Hours used:** More information needed
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+ - **Cloud Provider:** More information needed
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+ - **Compute Region:** More information needed
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+ - **Carbon Emitted:** More information needed
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+
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+ # Technical Specifications [optional]
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+
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+ ## Model Architecture and Objective
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+
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+ More information needed
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+
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+ ## Compute Infrastructure
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+
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+ More information needed
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+
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+ ### Hardware
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+ More information needed
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+
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+ ### Software
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+ More information needed.
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+
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+ # Citation
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+ More information needed.
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+
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+ # Glossary [optional]
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+ More information needed
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+
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+ # More Information [optional]
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+ More information needed
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+
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+
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+ # Model Card Authors [optional]
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+ Tatyana Voloshina in collaboration with Ezi Ozoani and the Hugging Face team
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+
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+ # Model Card Contact
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+ More information needed
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+ # How to Get Started with the Model
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+ Use the code below to get started with the model.
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+
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+ <details>
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+ <summary> Click to expand </summary>
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+
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+ Needed pytorch trained model presented in [Drive](https://drive.google.com/drive/folders/1EnJBq0dGfpjPxbVjybqaS7PsMaPHLUIl?usp=sharing).
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+
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+ Load and place model.pth.tar in folder next to another files of a model.
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+
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+ ```python
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+
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  !pip install tensorflow-gpu
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  !pip install deeppavlov
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  !python -m deeppavlov install squad_bert
 
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  model = build_model(path_to_model/rubert_sentiment.json)
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  model(["Сегодня хорошая погода", "Я счастлив проводить с тобою время", "Мне нравится эта музыкальная композиция"])
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+ ```
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+ </details>
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