|
---
|
|
license: mit
|
|
---
|
|
## Overview
|
|
The model takes a news article and predicts if it true or fake.
|
|
The format of the input should be:
|
|
|
|
```
|
|
<title> TITLE HERE <content> CONTENT HERE <end>
|
|
```
|
|
|
|
## Using this model in your code:
|
|
To use this model, first download it from the hugginface website:
|
|
```python
|
|
from transformers import AutoTokenizer, AutoModelForSequenceClassification
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained("hamzab/roberta-fake-news-classification")
|
|
|
|
model = AutoModelForSequenceClassification.from_pretrained("hamzab/roberta-fake-news-classification")
|
|
```
|
|
|
|
Then, make a prediction like follows:
|
|
```python
|
|
import torch
|
|
def predict_fake(title,text):
|
|
input_str = "<title>" + title + "<content>" + text + "<end>"
|
|
input_ids = tokenizer.encode_plus(input_str, max_length=512, padding="max_length", truncation=True, return_tensors="pt")
|
|
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
|
model.to(device)
|
|
with torch.no_grad():
|
|
output = model(input_ids["input_ids"].to(device), attention_mask=input_ids["attention_mask"].to(device))
|
|
return dict(zip(["Fake","Real"], [x.item() for x in list(torch.nn.Softmax()(output.logits)[0])] ))
|
|
|
|
print(predict_fake(<HEADLINE-HERE>,<CONTENT-HERE>))
|
|
```
|
|
You can also use Gradio to test the model on real-time:
|
|
```python
|
|
import gradio as gr
|
|
iface = gr.Interface(fn=predict_fake, inputs=[gr.inputs.Textbox(lines=1,label="headline"),gr.inputs.Textbox(lines=6,label="content")], outputs="label").launch(share=True)
|
|
``` |
