likeyellow/klue-review-star

klue-review-star

Successor: klue-review-star-4class — adds a fourth class that rejects non-review input.

Predicts a 1.0–5.0 star rating from Korean restaurant review text alone, along with a confidence score.

Fine-tuned from klue/bert-base on the KR3 dataset.

Why

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:

1. 1.3-class classification — negative / positive / neutral
2. 2.Star rating = expected value over class anchors [1.0, 5.0, 3.0]
3. 3.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

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.

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:

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.

Links

• —Serving API: https://github.com/likeyellow/review-star-api
• —Demo frontend: https://github.com/likeyellow/review-star-ai