BERT-absa-rest-reviews-asc
Document-level sentiment classifier for Russian restaurant reviews. Given the full text of a review, it predicts the overall sentiment: Positive, Negative, or Neutral.
This is a fine-tune of cointegrated/rubert-tiny2 (a compact Russian BERT, 29M parameters) for sequence classification. It complements the aspect extraction model billerjully/BERT-absa-rest-reviews-ate: together they answer "what is the review about" and "how does the reviewer feel overall".
Usage
from transformers import pipeline
asc = pipeline(
"text-classification",
model="billerjully/BERT-absa-rest-reviews-asc",
top_k=None, # return probabilities for all three classes
)
text = "Ужасное место. Еда пришла холодной, официант хамил, цены завышены."
print(asc(text)[0])
[{'label': 'Positive', 'score': 0.71},
{'label': 'Negative', 'score': 0.15},
{'label': 'Neutral', 'score': 0.14}]
Training data
Trained on billerjully/ru-absa-restaurant-reviews — 7,253 Russian restaurant reviews:
| Label | Count | Share |
|---|---|---|
| Positive | 6,163 | 85.0% |
| Negative | 591 | 8.1% |
| Neutral | 499 | 6.9% |
Limitations
- Document-level only; no aspect-level sentiment.
- Domain-specific (restaurant reviews) and Russian language only.
Related
- Aspect extraction model: billerjully/BERT-absa-rest-reviews-ate
- Dataset: billerjully/ru-absa-restaurant-reviews
- Base model: cointegrated/rubert-tiny2