---
library_name: transformers
base_model: bert-base-chinese
tags:
- generated_from_trainer
datasets:
- real-jiakai/chinese-squadv2
model-index:
- name: chinese_squadv2
  results: []
---

# bert-base-chinese-finetuned-squadv2

This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the [Chinese SQuAD v2.0 dataset](https://huggingface.co/datasets/real-jiakai/chinese-squadv2).

## Model Description

This model is designed for Chinese question answering tasks, specifically for extractive QA where the answer must be extracted from a given context paragraph. It can handle both answerable and unanswerable questions, following the SQuAD v2.0 format.

Key features:
- Based on BERT-base Chinese architecture
- Supports both answerable and unanswerable questions
- Trained on Chinese question-answer pairs
- Optimized for extractive question answering

## Intended Uses & Limitations

### Intended Uses
- Chinese extractive question answering
- Reading comprehension tasks
- Information extraction from Chinese text
- Automated question answering systems

### Limitations
- Performance is significantly better on unanswerable questions (76.65% accuracy) compared to answerable questions (36.41% accuracy)
- Limited to extractive QA (cannot generate new answers)
- May not perform well on domain-specific questions outside the training data
- Designed for modern Chinese text, may not work well with classical Chinese or dialectal variations

## Training and Evaluation Data

The model was trained on the Chinese SQuAD v2.0 dataset, which contains:

Training Set:
- Total examples: 90,027
- Answerable questions: 46,529
- Unanswerable questions: 43,498

Validation Set:
- Total examples: 9,936
- Answerable questions: 3,991
- Unanswerable questions: 5,945

## Training Procedure

### Training Hyperparameters

- Learning rate: 3e-05
- Batch size: 12
- Evaluation batch size: 8
- Number of epochs: 5
- Optimizer: AdamW (β1=0.9, β2=0.999, ε=1e-08)
- Learning rate scheduler: Linear
- Maximum sequence length: 384
- Document stride: 128
- Training device: CUDA-enabled GPU

### Training Results

Final evaluation metrics:
- Overall Exact Match: 60.49%
- Overall F1 Score: 60.54%
- Answerable Questions:
  - Exact Match: 36.41%
  - F1 Score: 36.53%
- Unanswerable Questions:
  - Exact Match: 76.65%
  - F1 Score: 76.65%

### Framework Versions
- Transformers: 4.47.0.dev0
- PyTorch: 2.5.1+cu124
- Datasets: 3.1.0
- Tokenizers: 0.20.3

## Usage

```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "real-jiakai/bert-base-chinese-finetuned-squadv2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)

# Prepare the inputs
question = "your_question"
context = "your_context"
inputs = tokenizer(
    question,
    context,
    add_special_tokens=True,
    return_tensors="pt"
)

# Get the answer
start_scores, end_scores = model(**inputs)
start_index = torch.argmax(start_scores)
end_index = torch.argmax(end_scores)
answer = tokenizer.convert_tokens_to_string(
    tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][start_index:end_index+1])
)
```

## Limitations and Bias

The model shows significant performance disparity between answerable and unanswerable questions, which might indicate:
1. Dataset quality issues
2. Potential translation artifacts in the Chinese version of SQuAD
3. Imbalanced handling of answerable vs. unanswerable questions

## Ethics & Responsible AI

Users should be aware that:
- The model may reflect biases present in the training data
- Performance varies significantly based on question type
- Results should be validated for critical applications
- The model should not be used as the sole decision-maker in critical systems

### Framework versions

- Transformers 4.47.0.dev0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3