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---
license: mit
language:
- en
- ja
- zh
- ko
metrics:
- accuracy
base_model: google-bert/bert-base-multilingual-cased
pipeline_tag: text-classification
tags:
- sex
- filename
- dectection
- content
- mbert
- Multilingual
---
# Model Card for Model ID
Detect sexual content in text or file names.
## Model Details
### Model Description
- **Developed by:** liu wei
- **License:** MIT
- **Finetuned from model:** bert-base-multilingual-cased
- **Task:** Simple Classification
- **Language:** Multilingual
- **Max Length:** 128
- **Updated Time:** 2024-8-22
### Model Training Information
- **Training Dataset Size:** 100,000 manually annotated data with noise
- **Data Distribution:** 50:50
- **Batch Size:** 8
- **Epochs:** 5
- **Accuracy:** 92%
- **F1:** 92%
## Uses
- Supports multiple languages, such as English, Chinese, Japanese, etc.
- Use for detect content submitted by users in forums, magnetic search engines, cloud disks, etc.
- Detect semantics and variant content, Porn movie numbers or variant file names.
- Compared with GPT4O-mini, The detection accuracy is greatly improved.
### Examples
- Example **English**
```python
predict("Tiffany Doll - Wine Makes Me Anal (31.03.2018)_1080p.mp4")
```
```json
{
"predictions": 1,
"label": "Sexual"
}
```
- Example **Chinese**
```python
predict("橙子 · 保安和女业主的一夜春宵。路见不平拔刀相助，救下苏姐，以身相许！")
```
```json
{
"predictions": 1,
"label": "Sexual"
}
```
- Example **Japanese**
```python
predict("MILK-217-UNCENSORED-LEAKピタコス Gカップ痴女 完全着衣で濃密5PLAY 椿りか 580 2.TS")
```
```json
{
"predictions": 1,
"label": "Sexual"
}
```
- Example **Porn Movie Numbers**
```python
predict("DVAJ-548_CH_SD")
```
```json
{
"predictions": 1,
"label": "Sexual"
}
```
## How to Get Started with the Model
### step 1:
Create a python file under this model, such as 'use_model.py'
```python
import torch
from transformers import BertForSequenceClassification, BertTokenizer
# load model
tokenizer = BertTokenizer.from_pretrained("uget/sexual_content_dection")
model = BertForSequenceClassification.from_pretrained("uget/sexual_content_dection")
def predict(text):
encoding = tokenizer(text, return_tensors="pt")
encoding = {k: v.to(model.device) for k,v in encoding.items()}
outputs = model(**encoding)
probs = torch.sigmoid(outputs.logits)
predictions = torch.argmax(probs, dim=-1)
label_map = {0: "None", 1: "Sexual"}
predicted_label = label_map[predictions.item()]
print(f"Predictions:{predictions.item()}, Label:{predicted_label}")
return {"predictions": predictions.item(), "label": predicted_label}
predict("Tiffany Doll - Wine Makes Me Anal (31.03.2018)_1080p.mp4")
```
### step 2:
Run
```shell
python3 use_model.py
```
Response JSON
```json
{
"predictions": 1,
"label": "Sexual"
}
```
### Explanation
The results only include two situations:
- predictions-0 **Not Dectection** sexual content;
- predictions-1 **Sexual** content was detected.
## Model Card Contact
Email: [email protected] |