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README.md

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+---
+license: apache-2.0
+base_model: roberta-base-gru
+tags:
+- vietnamese
+- aspect-based-sentiment-analysis
+- VLSP-ABSA
+datasets:
+- visolex/VLSP2018-ABSA-Restaurant
+metrics:
+- accuracy
+- macro-f1
+model-index:
+- name: roberta-gru-absa-restaurant
+  results:
+  - task:
+      type: text-classification
+      name: Aspect-based Sentiment Analysis
+    dataset:
+      name: VLSP2018-ABSA-Restaurant
+      type: VLSP-ABSA
+    metrics:
+    - type: accuracy
+      value: 0.8504
+    - type: macro-f1
+      value: 0.4915
+    - type: macro_precision
+      value: 0.6774
+    - type: macro_recall
+      value: 0.4149
+---
+
+# roberta-gru-absa-restaurant: Aspect-based Sentiment Analysis for Vietnamese Reviews
+
+This model is a fine-tuned version of [roberta-base-gru](https://huggingface.co/roberta-base-gru) 
+on the **VLSP2018-ABSA-Restaurant** dataset for aspect-based sentiment analysis in Vietnamese reviews.
+
+## Model Details
+
+* **Base Model**: roberta-base-gru
+* **Description**: RoBERTa-GRU hybrid model fine-tuned
+* **Dataset**: VLSP2018-ABSA-Restaurant
+* **Fine-tuning Framework**: HuggingFace Transformers
+* **Task**: Aspect-based Sentiment Classification (3 classes)
+
+### Hyperparameters
+
+* Batch size: `32`
+* Learning rate: `3e-5`
+* Epochs: `100`
+* Max sequence length: `256`
+* Weight decay: `0.01`
+* Warmup steps: `500`
+* Optimizer: AdamW
+
+## Dataset
+
+Model was trained on **VLSP2018 ABSA Restaurant dataset** for aspect-based sentiment analysis.
+
+### Sentiment Labels:
+
+* **0 - Negative** (Tiêu cực): Negative opinions
+* **1 - Neutral** (Trung lập): Neutral, objective opinions
+* **2 - Positive** (Tích cực): Positive opinions
+
+### Aspect Categories:
+
+Model được train để phân tích sentiment cho các aspects sau:
+
+- **AMBIENCE#GENERAL**
+- **DRINKS#PRICES**
+- **DRINKS#QUALITY**
+- **DRINKS#STYLE&OPTIONS**
+- **FOOD#PRICES**
+- **FOOD#QUALITY**
+- **FOOD#STYLE&OPTIONS**
+- **LOCATION#GENERAL**
+- **RESTAURANT#GENERAL**
+- **RESTAURANT#MISCELLANEOUS**
+- **RESTAURANT#PRICES**
+- **SERVICE#GENERAL**
+
+## Evaluation Results
+
+The model was evaluated on test set with the following metrics:
+
+* **Accuracy**: `0.8504`
+* **Macro-F1**: `0.4915`
+* **Weighted-F1**: `0.7004`
+* **Macro-Precision**: `0.6774`
+* **Macro-Recall**: `0.4149`
+
+## Usage Example
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModel
+
+# Load model and tokenizer
+repo = "visolex/roberta-gru-absa-restaurant"
+tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
+model = AutoModel.from_pretrained(repo, trust_remote_code=True)
+model.eval()
+
+# Aspect labels for VLSP2018-ABSA-Restaurant
+aspect_labels = [
+    "AMBIENCE#GENERAL",
+    "DRINKS#PRICES",
+    "DRINKS#QUALITY",
+    "DRINKS#STYLE&OPTIONS",
+    "FOOD#PRICES",
+    "FOOD#QUALITY",
+    "FOOD#STYLE&OPTIONS",
+    "LOCATION#GENERAL",
+    "RESTAURANT#GENERAL",
+    "RESTAURANT#MISCELLANEOUS",
+    "RESTAURANT#PRICES",
+    "SERVICE#GENERAL"
+]
+
+# Sentiment labels
+sentiment_labels = ["POSITIVE", "NEGATIVE", "NEUTRAL"]
+
+# Example review text
+text = "Nhà hàng có không gian đẹp, đồ ăn ngon nhưng giá hơi đắt."
+
+# Tokenize
+inputs = tokenizer(
+    text,
+    return_tensors="pt",
+    padding=True,
+    truncation=True,
+    max_length=256
+)
+inputs.pop("token_type_ids", None)
+
+# Predict
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# Get logits: shape [1, num_aspects, num_sentiments + 1]
+logits = outputs.logits.squeeze(0)  # [num_aspects, num_sentiments + 1]
+probs = torch.softmax(logits, dim=-1)
+
+# Predict for each aspect
+none_id = probs.size(-1) - 1  # Index of "none" class
+results = []
+
+for i, aspect in enumerate(aspect_labels):
+    prob_i = probs[i]
+    pred_id = int(prob_i.argmax().item())
+    
+    if pred_id != none_id and pred_id < len(sentiment_labels):
+        score = prob_i[pred_id].item()
+        if score >= 0.5:  # threshold
+            results.append((aspect, sentiment_labels[pred_id].lower()))
+
+print(f"Text: {text}")
+print(f"Predicted aspects: {results}")
+# Output example: [('aspects', 'positive'), ('aspects', 'positive'), ('aspects', 'negative')]
+```
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{visolex_absa_roberta_gru_absa_restaurant,
+  title={RoBERTa-GRU hybrid model fine-tuned for Vietnamese Aspect-based Sentiment Analysis},
+  author={ViSoLex Team},
+  year={2025},
+  url={https://huggingface.co/visolex/roberta-gru-absa-restaurant}
+}
+```
+
+## License
+
+This model is released under the Apache-2.0 license.
+
+## Acknowledgments
+
+* Base model: [roberta-base-gru](https://huggingface.co/roberta-base-gru)
+* Dataset: VLSP2018-ABSA-Restaurant
+* ViSoLex Toolkit
+
+---