AtsushiHatake/ai-agent-sme-sentiment-mbert

Hospitality Review Sentiment mBERT

This is a three-class Japanese/English sentiment classifier for short hospitality reviews, fine-tuned from google-bert/bert-base-multilingual-cased. It categorizes each review as negative, neutral, or positive.

Intended use

The model predicts one overall label for a short Japanese or English hospitality review:

It is intended for research, prototyping, and human-reviewed analytics. It must not be used as the sole basis for consequential business, employment, moderation, or customer-service decisions. Applications using this model should allow operators to review and correct its predictions.

Training data

The model was fine-tuned on 480 synthetic bilingual reviews: 240 Japanese and 240 English, balanced across the three labels. No real customer reviews were collected or used.

The data was produced by a deterministic, template-based generator. Training and test splits use disjoint sentence templates, but they share the same small set of polarity-bearing fragments. This shared vocabulary can substantially inflate performance compared with genuinely novel reviews.

Evaluation

The exact weights in this directory were re-evaluated on the 240-item synthetic test split (120 Japanese and 120 English; 80 items per class):

The published weights achieved macro-F1 1.000 on a 240-item synthetic test split. Training and test templates were disjoint, but polarity-bearing fragment vocabulary was shared. The result therefore measures performance within the generator's narrow distribution and must not be interpreted as real-world deployment accuracy.

The zero-shot comparison baseline achieved macro-F1 0.664 on the same split.

Known limitations

• —The model has not been evaluated on an independently annotated set of genuine restaurant reviews.
• —Synthetic training and test text has narrow vocabulary and simple sentence structure.
• —Japanese/English code-switching and aspect-level sentiment were not evaluated.
• —A single overall label can conceal mixed opinions such as positive food but negative service.
• —Softmax confidence is not a calibrated probability of correctness on real reviews.
• —The unseen short review ramen was good is incorrectly classified as negative with approximately 0.966 confidence by these weights. In the synthetic training vocabulary, good occurs inside the neutral phrase neither good nor bad, illustrating generator-specific lexical learning.

Example

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="AtsushiHatake/ai-agent-sme-sentiment-mbert",
)

print(classifier("料理がおいしく、スタッフも親切でした。"))  # Japanese
print(classifier("The food was delicious, and the staff were very friendly."))  # English

# Response examples:
# [{'label': 'positive', 'score': 0.9935460686683655}]
# [{'label': 'positive', 'score': 0.993598461151123}]

The score may differ slightly depending on the hardware and software environment.

Reproducibility

The data generator, training and evaluation scripts, synthetic data, and recorded results are available in the source repository:

<https://github.com/atsushi729/ai-agent-sme>

Consumers should pin a Hugging Face commit revision rather than loading an unversioned main branch.

Licence and attribution

The base BERT model and the fine-tuned distribution are provided under the Apache License 2.0. See LICENSE and NOTICE. This is a modified model: multilingual BERT was fine-tuned for three-class hospitality-review sentiment on the synthetic dataset described above. The release is not an official Google product.