Upload README.md

README.md

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+# 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.
+
+---
+
+## 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
+
+---
+
+## 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}")
+```
+
+---
+
+## Performance Metrics
+
+- **Accuracy:** 0.970222
+- **F1 Score:** 0.964973  
+- **Training Loss:** 0.130104  
+- **Validation Loss:** 0.066986
+
+---
+
+## 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.
+
+---
+
+## 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
+```
+
+---
+
+## 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)
+```
+
+---
+
+## 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
+
+---
+
+## 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
+
+---
+
+## 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
+
+---
+
+## Contributing
+
+Contributions are welcome! Feel free to open an issue or PR for improvements, fixes, or feature extensions.
+
+---