Aspect Term Extraction (ATE) model for Russian restaurant reviews. Given a review, it finds aspect terms (words the reviewer is talking about) and assigns each to one of five categories: Food, Service, Price, Interior, Delivery.

This is a fine-tune of cointegrated/rubert-tiny2 (a compact Russian BERT, 29M parameters) for token classification with BIO tagging. It is the first stage of an Aspect-Based Sentiment Analysis (ABSA) pipeline; see the companion sentiment model billerjully/BERT-absa-rest-reviews-asc.

Labels

Tag	Category	Examples of terms
`B-Food` / `I-Food`	Food	паста, стейк, порции, суп
`B-Service` / `I-Service`	Service	официант, персонал, обслуживание
`B-Price` / `I-Price`	Price	цены, счёт, стоимость
`B-Interior` / `I-Interior`	Interior	интерьер, музыка, атмосфера
`B-Delivery` / `I-Delivery`	Delivery	доставка, курьер, заказ
`O`	—	everything else

Usage

python

from transformers import pipeline

ate = pipeline(
    "token-classification",
    model="billerjully/BERT-absa-rest-reviews-ate",
    aggregation_strategy="simple",  # merges B-/I- tokens into whole terms
)

text = "Паста была превосходной, но официант нас совсем забыл, а цены завышены."
for aspect in ate(text):
    print(aspect["entity_group"], text[aspect["start"]:aspect["end"]], round(aspect["score"], 3))

Food паста 0.98
Service официант 0.98
Price цены 0.92

Tip: offsets (start/end) are Python code-point indices. If you highlight spans in JavaScript, slice the text as an array of code points (Array.from(text)) — emoji in reviews shift UTF-16 indices otherwise.

Training data

Trained on billerjully/ru-absa-restaurant-reviews — 7,253 Russian restaurant reviews with token-level BIO annotation. Aspect mention counts:

Category	Mentions
Food	9,481
Service	4,985
Interior	2,892
Price	1,087
Delivery	849

Limitations

Domain-specific: trained only on restaurant reviews; quality degrades on other domains.
Mostly single-token aspects: I- tags are very rare in the training data (< 60 occurrences), so multi-word aspect terms are seldom produced.
Recall is imperfect: the model is small (29M params) and may miss some aspects, especially rare Price/Delivery mentions.
Russian language only.

Sentiment model: billerjully/BERT-absa-rest-reviews-asc
Dataset: billerjully/ru-absa-restaurant-reviews
Base model: cointegrated/rubert-tiny2

billerjully/BERT-absa-rest-reviews-ate

BERT-absa-rest-reviews-ate

Labels

Usage

Training data

Limitations

Related

billerjully/
BERT-absa-rest-reviews-ate