RuBERT for Sentiment Analysis of Product Reviews
This is a DeepPavlov/rubert-base-cased-conversational model trained on RuReviews.
Labels
0: NEUTRAL
1: POSITIVE
2: NEGATIVE
How to use
import torch
from transformers import AutoModelForSequenceClassification
from transformers import BertTokenizerFast
tokenizer = BertTokenizerFast.from_pretrained('blanchefort/rubert-base-cased-sentiment-rurewiews')
model = AutoModelForSequenceClassification.from_pretrained('blanchefort/rubert-base-cased-sentiment-rurewiews', 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
RuReviews: An Automatically Annotated Sentiment Analysis Dataset for Product Reviews in Russian.
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