README.md

# BERT-Base Quantized Model for Relation Extraction

This repository hosts a quantized version of the BERT-Base Chinese model, fine-tuned for relation extraction tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments.

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## Model Details

- **Model Name:** BERT-Base Chinese   
- **Model Architecture:** BERT Base  
- **Task:** Relation Extraction/Classification  
- **Dataset:** Chinese Entity-Relation Dataset  
- **Quantization:** Float16  
- **Fine-tuning Framework:** Hugging Face Transformers

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## Usage

### Installation

```bash
pip install transformers torch evaluate
```

### Loading the Quantized Model

```python
from transformers import BertTokenizerFast, BertForSequenceClassification
import torch

# Load the fine-tuned model and tokenizer
model_path = "final_relation_extraction_model"
tokenizer = BertTokenizerFast.from_pretrained(model_path)
model = BertForSequenceClassification.from_pretrained(model_path)
model.eval()

# Example input with entity markers
text = "笔名：[SUBJ] 木斧 [/SUBJ] 出生地：[OBJ] 成都 [/OBJ]"

# Tokenize input
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)

# Inference
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    predicted_class = torch.argmax(logits, dim=1).item()

# Map prediction to relation label
label_mapping = {0: "出生地", 1: "出生日期", 2: "民族", 3: "职业"}  # Customize based on your labels
predicted_relation = label_mapping[predicted_class]
print(f"Predicted Relation: {predicted_relation}")
```

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## Performance Metrics

- **Accuracy:** 0.970222
- **F1 Score:** 0.964973  
- **Training Loss:** 0.130104  
- **Validation Loss:** 0.066986

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## Fine-Tuning Details

### Dataset

The model was fine-tuned on a Chinese entity-relation dataset with:
- Entity pairs marked with special tokens `[SUBJ]` and `[OBJ]`
- Text preprocessed to include entity boundaries
- Multiple relation types including biographical information

### Training Configuration

- **Epochs:** 3  
- **Batch Size:** 16  
- **Learning Rate:** 2e-5  
- **Max Length:** 128 tokens   
- **Evaluation Strategy:** epoch  
- **Weight Decay:** 0.01
- **Optimizer:** AdamW

### Data Processing

The original SPO (Subject-Predicate-Object) format was converted to relation classification:
- Each SPO triple becomes a separate training example
- Entities are marked with special tokens in the text
- Relations are encoded as numerical labels for classification

### Quantization

Post-training quantization was applied using PyTorch's Float16 precision to reduce the model size and improve inference efficiency.

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## Repository Structure

```
.
├── final_relation_extraction_model/
│   ├── config.json           
│   ├── pytorch_model.bin     # Fine-tuned Model
│   ├── tokenizer_config.json    
│   ├── special_tokens_map.json
│   ├── tokenizer.json
│   ├── vocab.txt
│   └── added_tokens.json
├── relationship-extraction.ipynb  # Training notebook
└── README.md                     # Model documentation
```

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## Entity Marking Format

The model expects input text with entities marked using special tokens:
- Subject entities: `[SUBJ] entity_name [/SUBJ]`
- Object entities: `[OBJ] entity_name [/OBJ]`

Example:
```
Input: "笔名：[SUBJ] 木斧 [/SUBJ]原名：杨莆曾民族： [OBJ] 回族 [/OBJ]"
Output: "民族" (ethnicity relation)
```

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## Supported Relations

The model can classify various biographical and factual relations in Chinese text, including:
- 出生地 (Birthplace)
- 出生日期 (Birth Date)  
- 民族 (Ethnicity)
- 职业 (Occupation)
- And many more based on the training dataset

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## Limitations

- The model is specifically trained for Chinese text and may not work well with other languages
- Performance depends on proper entity marking in the input text
- The model may not generalize well to domains outside the fine-tuning dataset
- Quantization may result in minor accuracy degradation compared to full-precision models

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## Training Environment

- **Platform:** Kaggle Notebooks with GPU acceleration
- **GPU:** NVIDIA Tesla T4
- **Training Time:** Approximately 1 hour 5 minutes
- **Framework:** Hugging Face Transformers with PyTorch backend

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## Contributing

Contributions are welcome! Feel free to open an issue or PR for improvements, fixes, or feature extensions.

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