## Sentiment model based on rubert-base-cased-conversational
This model was initialized with [rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) weights and trained on a batch of datasets collected by [Smetanin](https://duckduckgo.com), using the same training sampling presented in [this wonderful work](https://huggingface.co/cointegrated/rubert-tiny-sentiment-balanced). This approach allows for a uniform distribution among different datasets and three classes of sentiment labels: negative, neutral, and positive. Datasets were prepared by David Dale and are hosted [here](https://drive.google.com/file/d/1dir_lixYfReDXxRS5oGGljH8T_f7vVqm/view).

I chose rubert-base-cased-conversational weights because, according to Smetanin's work, this model ranks first among all other multilingual and popular Russian language models with BERT base architecture.

### Training and Testing Details
This model was trained and tested using the code and hyperparameters from the [rubert-tiny-sentiment-balanced](https://huggingface.co/cointegrated/rubert-tiny-sentiment-balanced) work.

### Labels
There are only three labels: negative - 0, neutral - 1, positive - 2

## Results
It outperforms rubert-tiny-sentiment-balanced on four datasets, underperforms on one (linis), and has the same performance on mokoron and rureviews. See [this](https://huggingface.co/cointegrated/rubert-tiny-sentiment-balanced) for the comparison.

| Source | Macro F1 |
| ----------- | ----------- |
| SentiRuEval2016_banks | 0.88 | 
| SentiRuEval2016_tele  | 0.79 |
| kaggle_news | 0.73 |
| linis | 0.46 |
| mokoron | 0.98 |
| rureviews | 0.77 |
| rusentiment | 0.74 |
{"language": ["ru"], "tags": ["russian", "classification", "sentiment", "multiclass"], "widget": [{"text": "\u041c\u043d\u0435 \u043e\u0447\u0435\u043d\u044c \u0436\u0430\u043b\u044c"}]}
transformers pytorch safetensors bert text-classification russian classification sentiment multiclass ru autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #text-classification #russian #classification #sentiment #multiclass #ru #autotrain_compatible #endpoints_compatible #region-us 

Sentiment model based on rubert-base-cased-conversational
---------------------------------------------------------


This model was initialized with rubert-base-cased-conversational weights and trained on a batch of datasets collected by Smetanin, using the same training sampling presented in this wonderful work. This approach allows for a uniform distribution among different datasets and three classes of sentiment labels: negative, neutral, and positive. Datasets were prepared by David Dale and are hosted here.


I chose rubert-base-cased-conversational weights because, according to Smetanin's work, this model ranks first among all other multilingual and popular Russian language models with BERT base architecture.


### Training and Testing Details


This model was trained and tested using the code and hyperparameters from the rubert-tiny-sentiment-balanced work.


### Labels


There are only three labels: negative - 0, neutral - 1, positive - 2


Results
-------


It outperforms rubert-tiny-sentiment-balanced on four datasets, underperforms on one (linis), and has the same performance on mokoron and rureviews. See this for the comparison.




### Training and Testing Details


This model was trained and tested using the code and hyperparameters from the rubert-tiny-sentiment-balanced work. ### Labels


There are only three labels: negative - 0, neutral - 1, positive - 2


Results
-------


It outperforms rubert-tiny-sentiment-balanced on four datasets, underperforms on one (linis), and has the same performance on mokoron and rureviews. See this for the comparison.
TAGS
#transformers #pytorch #safetensors #bert #text-classification #russian #classification #sentiment #multiclass #ru #autotrain_compatible #endpoints_compatible #region-us 
 ### Training and Testing Details


This model was trained and tested using the code and hyperparameters from the rubert-tiny-sentiment-balanced work. ### Labels


There are only three labels: negative - 0, neutral - 1, positive - 2


Results
-------


It outperforms rubert-tiny-sentiment-balanced on four datasets, underperforms on one (linis), and has the same performance on mokoron and rureviews. See this for the comparison.
TAGS
#transformers #pytorch #safetensors #bert #text-classification #russian #classification #sentiment #multiclass #ru #autotrain_compatible #endpoints_compatible #region-us 
### Training and Testing Details


This model was trained and tested using the code and hyperparameters from the rubert-tiny-sentiment-balanced work.### Labels


There are only three labels: negative - 0, neutral - 1, positive - 2


Results
-------


It outperforms rubert-tiny-sentiment-balanced on four datasets, underperforms on one (linis), and has the same performance on mokoron and rureviews. See this for the comparison.