In Korea, "rating terrorism" is a recurring problem: users leave positive review text ("굿", "친절해요") while assigning one star, dragging a restaurant's average down. Because the text contains nothing abusive or false, platforms cannot moderate it.

This model removes the separate star input: the rating is derived from the review body, so the two cannot diverge.

How it works

KR3 ships categorical labels (0 negative, 1 positive, 2 ambiguous) rather than the original star values, so direct regression is not possible.

Instead:

3-class classification — negative / positive / neutral
Star rating = expected value over class anchors [1.0, 5.0, 3.0]
Confidence = 1 − H(p) / ln(3), the normalized entropy of the same distribution, inverted

Step 3 costs nothing extra and identifies mixed reviews: text combining praise and complaint produces a spread-out distribution and therefore low confidence. Downstream, this is used to down-weight ambiguous reviews when aggregating a restaurant's average.

Usage

python

import numpy as np
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

MODEL = "likeyellow/klue-review-star"
ANCHOR = np.array([1.0, 5.0, 3.0])  # [negative, positive, neutral]

tok = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL).eval()

def predict(text):
    enc = tok(text, truncation=True, max_length=256, return_tensors="pt")
    with torch.no_grad():
        p = torch.softmax(model(**enc).logits, dim=-1)[0].numpy()
    star = float((p * ANCHOR).sum())
    ent = float(-(p * np.log(p + 1e-9)).sum())
    return round(star, 2), round(1 - ent / np.log(3), 3)

print(predict("굿"))
# (4.7, 0.607)

print(predict("회는 신선한데 주차가 너무 불편했어요"))
# (3.6, 0.15)   ← mixed review, low confidence

print(predict("재료가 다 상한 것 같고 직원도 불친절했어요 최악"))
# (1.02, 0.958)

Results

Test set: 15,000 reviews, stratified.

Metric	Value
Accuracy	0.768
Macro F1	0.751
Star MAE	0.640
Negative recall	0.846
Neutral recall	0.626

Star MAE is the primary metric: misclassifying positive as neutral is a full error under classification, but only a small error in star terms. MAE reflects what the system is actually for.

Model comparison

beomi/KcELECTRA-base, pretrained on colloquial Korean comment data, was evaluated on the same split:

	klue/bert-base	KcELECTRA-base
Accuracy	0.768	0.755
Star MAE	0.640	0.656
Neutral recall	0.626	0.657
Negative recall	0.846	0.857

KcELECTRA is better on minority classes but worse on overall accuracy and star MAE. Since star accuracy is the objective, klue/bert-base was chosen. KcELECTRA's validation loss was still decreasing at epoch 2, so it may overtake with longer training.

Training

Data: 150,000 reviews stratified from KR3, split 80/10/10
Class weights [3.01, 0.55, 1.17] to correct 11 / 60 / 28 imbalance
2 epochs, lr 2e-5, batch 32, max_len 256, fp16, NVIDIA T4
Best checkpoint selected by validation loss (epoch 1)

Limitations

Out-of-domain input. Text unrelated to restaurants is still forced into one of three classes and tends to drift positive, following the training prior. Confidence drops accordingly (~0.2), which is the intended signal, but no explicit rejection class exists.
Label noise. KR3's ambiguous class mixes genuinely neutral, purely informational, and clearly positive reviews. Neutral recall of 0.626 reflects this.
Understated hedging. Softly negative phrasing tends to score lower than a human would rate it.
Confidence is not accuracy. It measures how concentrated the model's distribution is, not how often it is right. No calibration was performed.

License

CC BY-NC-SA 4.0, inherited from the KR3 dataset. Non-commercial use only.

likeyellow/klue-review-star

klue-review-star

Why