Senti-RoBERTa-Mini

Chinese Movie Review Sentiment Classification Model (5-Star Rating)

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1. Model Overview

H-Z-Ning/Senti-RoBERTa-Mini is a lightweight Chinese RoBERTa model fine-tuned specifically for assigning 1-to-5-star sentiment ratings to Chinese movie short reviews. Built on the HFL-Tencent hfl/chinese-roberta-wwm-ext checkpoint, it retains a small footprint and fast inference, making it ideal for resource-constrained deployments.

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2. Model Facts

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3. Quick Start

3.1 Install Dependencies

pip install transformers torch

3.2 One-Line Inference

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

repo = "H-Z-Ning/Senti-RoBERTa-Mini"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForSequenceClassification.from_pretrained(repo)

text = "这个导演真厉害。"
inputs = tok(text, return_tensors="pt", truncation=True, max_length=256)
with torch.no_grad():
    logits = model(**inputs).logits
pred = int(torch.argmax(logits, dim=-1).item()) + 1  # 1..5
print("predicted rating:", pred)

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4.Training source code

senti-roberta-mini training source code

5. Training Details

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6. Citation

@misc{senti-roberta-mini-2025,
  title={Senti-RoBERTa-Mini: A Mini Chinese RoBERTa for Movie Review Rating},
  author={H-Z-Ning},
  year={2025},
  howpublished={\url{https://huggingface.co/H-Z-Ning/Senti-RoBERTa-Mini}}
}

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7. License

This model is released under Apache-2.0. The base model hfl/chinese-roberta-wwm-ext is also Apache-2.0.

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Community contributions and feedback are welcome! If you encounter any issues, please open an Issue or email the author.