Update README.md

README.md

@@ -14,6 +14,144 @@ tags:
 - filename
 - dectection
 - content
-- bert
 - 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%
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## 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:  
+Save all the model files into a directory, such as 'model'
+### step 2: 
+Create a python file under this model, such as 'use_model.py'
+```python
+import torch
+from transformers import BertForSequenceClassification, BertTokenizer
+# load model
+model = BertForSequenceClassification.from_pretrained("./sexual_detection")
+# load tokenizer
+tokenizer = BertTokenizer.from_pretrained("./sexual_detection")
+
+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 3:
+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]