---
library_name: transformers
language: zh
tags:
- violence-detection
- sequence-classification
license: apache-2.0
widget:
- text: "在事件前，他公然向行凶暴徒的反华集会，发表煽动反华排华的讲话"
- text: "今天天气那么好，我们出去玩吧！"
- text: "公共交通工具使用率越来越高，人们更愿意选择绿色出行方式。"
---

# BERT Violence Detection Model

This model is fine-tuned for detecting violent and non-violent content in Chinese text using BERT. It is based on the research presented in the paper ["Vectors of Violence: Legitimation and Distribution of State Power in the *People's Liberation Army Daily* (*Jiefangjun Bao*), 1956-1989"](https://doi.org/10.22148/001c.115481) by Aaron Gilkison and Maciej Kurzynski.

## Model Description

The model is based on `bert-base-chinese` and fine-tuned for sequence classification with two labels: `non-violent` (0) and `violent` (1). The fine-tuning corpus included a large number of articles from the *People's Liberation Army Daily* (*Jiefangjun Bao*, or *JFJB*) which is one of the official newspapers in the People's Republic of China. 
Further updates to the model are expected in the future.

## Usage

Here’s how you can use this model with the Transformers library:

```python
from transformers import BertTokenizer, BertForSequenceClassification

model_name = "qhchina/BERT-JFJB-violence-0.1"
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name)

sentence = "今天天气那么好，我们出去玩吧"

inputs = tokenizer(sentence, return_tensors="pt")

outputs = model(**inputs)
logits = outputs.logits

# Get the predicted label
predicted_label = logits.argmax(-1).item()
label_mapping = {0: "non-violent", 1: "violent"}
predicted_label_name = label_mapping[predicted_label]

print(f"Sentence: {sentence}")
print(f"Predicted label: {predicted_label_name}")
```

## Citing the Paper

If you use this model in your research, please kindly cite the following paper:

Gilkison, Aaron, and Maciej Kurzynski. "Vectors of Violence: Legitimation and Distribution of State Power in the *People's Liberation Army Daily* (*Jiefangjun Bao*), 1956-1989." *Journal of Cultural Analytics*, vol. 9, no. 1, May 2024, [https://doi.org/10.22148/001c.115481](https://doi.org/10.22148/001c.115481).