Four-class classifier for real Korean Naver Shopping ratings 1, 2, 4, and 5. This is not a movie model. Rating 3 is absent from the source dataset, so the model cannot predict it.

Source code, full reproduction pipeline, and a from-scratch CORAL ordinal-regression comparison (which this model outperforms — see below): GitHub. A Gradio demo lives in that repo's space/ folder and loads this model straight from the Hub, but there is no hosted demo — Hugging Face requires a PRO subscription to run Gradio Spaces, so the demo is local-only.

Results

All rows use the same deterministic 19,982-review test split, with 95% bootstrap confidence intervals. This model is the CE + argmax row — loadable with stock AutoModelForSequenceClassification, no custom code.

System	MAE	Exact accuracy	QWK
Constant: training median	1.5859	9.39%	0.000
Majority rating class	1.7735	40.62%	0.000
This model (CE + argmax)	0.3856 [0.376, 0.396]	71.66% [71.0, 72.3]	0.837
CE + median decoder (same model, different decode rule)	0.3858	70.88%	0.842
CORAL ordinal regression (separate model, not published here)	0.5632	56.89%	0.810

The best checkpoint is epoch 2, selected on validation MAE (0.3894). Full per-class breakdown, confusion matrices, the CORAL comparison, and the decoder analysis (argmax vs. expected value vs. median — an earlier version of this project incorrectly assumed expected value minimizes MAE; it minimizes RMSE instead) are in the README.

Known weakness: rating 4 (9.4% of the data) has only 13.5% recall — the model rarely predicts it. This is not hidden by the aggregate MAE.

Data and provenance

Source: bab2min/corpus Naver Shopping sentiment file. The upstream repository declares the corpus Public Domain.

Pinned source SHA-256: dc4d1aca0a148671cbe80bcb81962eee297370acab42be93c1617ce9336479c0

The source contains 200,000 real source-file ratings. After excluding all 184 rows associated with 92 identical texts that have conflicting ratings, the splits are 159,852 train / 19,982 validation / 19,982 test. Targets are never derived from sentiment labels or generated synthetically.

Use

python

import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_id = "cringepnh/koelectra-korean-shopping-rating"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id).eval()
inputs = tokenizer("배송도 빠르고 제품도 정말 좋아요", return_tensors="pt", truncation=True)
with torch.inference_mode():
    probabilities = torch.softmax(model(**inputs).logits, dim=-1)[0]
index = int(probabilities.argmax())
print(model.config.id2label[index], float(probabilities[index]))

Limitations

Naver Shopping domain only; not movie reviews.
No 3-star examples or output class.
Curated class priors. The upstream corpus dropped 3-star reviews as ambiguous and sampled positive (4–5) against negative (1–2) to roughly 1:1. Real Naver Shopping ratings are strongly 5-star-skewed, so this model is miscalibrated for the natural distribution and will over-predict low ratings on live traffic. Apply prior correction before deploying.
Rating 4 has 13.5% recall under this model's argmax decoder — a real weakness, not smoothed over by MAE.
This uses ordinary 4-class cross-entropy with an argmax decoder by default. A from-scratch CORAL ordinal-regression model was trained as a comparison and performed worse (MAE 0.5632 vs. 0.3856); see the GitHub README for the full result and the likely mechanism (CORAL's shared projection collapses per-class recall toward the two extreme ratings).
Code is MIT; dataset licensing is the upstream repository's separate Public Domain declaration.