
# RuBERT for Sentiment Analysis

This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on [RuSentiment](http://text-machine.cs.uml.edu/projects/rusentiment/).

## Labels
    0: NEUTRAL
    1: POSITIVE
    2: NEGATIVE

## How to use
```python

import torch
from transformers import AutoModelForSequenceClassification
from transformers import BertTokenizerFast

tokenizer = BertTokenizerFast.from_pretrained('blanchefort/rubert-base-cased-sentiment-rusentiment')
model = AutoModelForSequenceClassification.from_pretrained('blanchefort/rubert-base-cased-sentiment-rusentiment', return_dict=True)

@torch.no_grad()
def predict(text):
    inputs = tokenizer(text, max_length=512, padding=True, truncation=True, return_tensors='pt')
    outputs = model(**inputs)
    predicted = torch.nn.functional.softmax(outputs.logits, dim=1)
    predicted = torch.argmax(predicted, dim=1).numpy()
    return predicted
```


## Dataset used for model training

**[RuSentiment](http://text-machine.cs.uml.edu/projects/rusentiment/)**

> A. Rogers A. Romanov A. Rumshisky S. Volkova M. Gronas A. Gribov RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian. Proceedings of COLING 2018.
{"language": ["ru"], "tags": ["sentiment", "text-classification"], "datasets": ["RuSentiment"]}
transformers pytorch tf jax safetensors bert text-classification sentiment ru dataset:RuSentiment autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tf #jax #safetensors #bert #text-classification #sentiment #ru #dataset-RuSentiment #autotrain_compatible #endpoints_compatible #has_space #region-us 


# RuBERT for Sentiment Analysis

This is a DeepPavlov/rubert-base-cased-conversational model trained on RuSentiment.

## Labels
    0: NEUTRAL
    1: POSITIVE
    2: NEGATIVE

## How to use



## Dataset used for model training

RuSentiment

> A. Rogers A. Romanov A. Rumshisky S. Volkova M. Gronas A. Gribov RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian. Proceedings of COLING 2018.
# RuBERT for Sentiment Analysis

This is a DeepPavlov/rubert-base-cased-conversational model trained on RuSentiment. ## Labels
    0: NEUTRAL
    1: POSITIVE
    2: NEGATIVE ## How to use ## Dataset used for model training

RuSentiment

> A. Rogers A. Romanov A. Rumshisky S. Volkova M. Gronas A. Gribov RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian. Proceedings of COLING 2018.
TAGS
#transformers #pytorch #tf #jax #safetensors #bert #text-classification #sentiment #ru #dataset-RuSentiment #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # RuBERT for Sentiment Analysis

This is a DeepPavlov/rubert-base-cased-conversational model trained on RuSentiment. ## Labels
    0: NEUTRAL
    1: POSITIVE
    2: NEGATIVE ## How to use ## Dataset used for model training

RuSentiment

> A. Rogers A. Romanov A. Rumshisky S. Volkova M. Gronas A. Gribov RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian. Proceedings of COLING 2018.
TAGS
#transformers #pytorch #tf #jax #safetensors #bert #text-classification #sentiment #ru #dataset-RuSentiment #autotrain_compatible #endpoints_compatible #has_space #region-us 
# RuBERT for Sentiment Analysis

This is a DeepPavlov/rubert-base-cased-conversational model trained on RuSentiment.## Labels
    0: NEUTRAL
    1: POSITIVE
    2: NEGATIVE## How to use## Dataset used for model training

RuSentiment

> A. Rogers A. Romanov A. Rumshisky S. Volkova M. Gronas A. Gribov RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian. Proceedings of COLING 2018.