H-Z-Ning
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Senti-RoBERTa-Mini

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+**Chinese Movie Review Sentiment Classification Model (5-Star Rating)**
+---
+## 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.
+---
+## 2. Model Facts
+| Item | Details |
+|---|---|
+| Task | Chinese text classification (sentiment / star rating) |
+| Labels | 5 classes (1 star – 5 stars) |
+| Base model | [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) |
+| Dataset | [Kaggle: Douban Movie Short Comments (140 K)](https://www.kaggle.com/datasets/utmhikari/doubanmovieshortcomments) |
+| Training framework | 🤗 transformers + Trainer |
+| Language | Simplified Chinese |
+| Parameters | ≈ 102 M (same as base model) |
+---
+## 3. Quick Start
+### 3.1 Install Dependencies
+```bash
+pip install transformers torch
+```
+### 3.2 One-Line Inference
+```python
+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)
+```
+---
+## 4. Training Details
+| Hyper-parameter | Value |
+|---|---|
+| Base model | hfl/chinese-roberta-wwm-ext |
+| Training framework | 🤗 transformers `Trainer` |
+| Training set | 50 000 samples (randomly drawn from 140 K) |
+| Validation set | 5 000 samples (same random draw) |
+| Test set | full original test set |
+| Max sequence length | 256 |
+| Training epochs | 3 |
+| Batch size | 32 (train) / 64 (eval) |
+| Learning rate | 2e-5 |
+| Optimizer | AdamW |
+| Weight decay | 0.01 |
+| Scheduler | linear warmup (warmup_ratio=0.1) |
+| Precision | FP16 |
+| Best-model criterion | **QWK (↑)** |
+| Training time | ≈ 32 min on single P100 (FP16) |
+| Logging interval | every 10 steps |
+---
+## 5. Citation
+```bibtex
+@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}}
+}
+```
+---
+## 6. License
+This model is released under [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0). The base model `hfl/chinese-roberta-wwm-ext` is also Apache-2.0.
+---
+Community contributions and feedback are welcome! If you encounter any issues, please open an [Issue](https://huggingface.co/H-Z-Ning/Senti-RoBERTa-Mini/discussions) or email the author.