transformers pytorch safetensors bert text-classification sentiment ru dataset:Tatyana/ru_sentiment_dataset arxiv:1910.09700 autotrain_compatible endpoints_compatible region:us
---
datasets:
- Tatyana/ru_sentiment_dataset
language:
- ru
tags:
- sentiment
- text-classification
---


# 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>

