
Модель [RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased) которая был fine-tuned на задачу __sentiment classification__ для коротких __Russian__ текстов.
Задача представляет собой __multi-class classification__ со следующими метками:

```yaml
0: neutral
1: positive
2: negative
```

## Usage

```python
from transformers import pipeline
model = pipeline(model="r1char9/rubert-base-cased-russian-sentiment")
model("Привет, ты мне нравишься!")
# [{'label': 'positive', 'score': 0.8220236897468567}]
```

## Dataset

Модель была натренирована на данных:

- Kaggle Russian News Dataset
- Linis Crowd 2015
- Linis Crowd 2016
- RuReviews
- RuSentiment

```yaml
tokenizer.max_length: 256
batch_size: 32
optimizer: adam
lr: 0.00001
weight_decay: 0
epochs: 2
```
{"language": ["ru"], "license": "mit", "tags": ["sentiment-analysis", "multi-class-classification", "sentiment analysis", "rubert", "sentiment", "bert", "russian", "multiclass", "classification"], "metrics": ["f1", "roc_auc", "precision", "recall"], "pipeline_tag": "text-classification"}
transformers pytorch bert text-classification sentiment-analysis multi-class-classification sentiment analysis rubert sentiment russian multiclass classification ru license:mit autotrain_compatible endpoints_compatible region:us