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--- |
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license: mit |
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widget: |
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- text: "Some ninja attacked the White House." |
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example_title: "Fake example 1" |
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language: |
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- en |
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tags: |
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- classification |
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datasets: |
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- "https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset" |
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--- |
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## Overview |
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The model is a `roberta-base` fine-tuned on [fake-and-real-news-dataset](https://www.kaggle.com/datasets/clmentbisaillon/fake-and-real-news-dataset). It has a 100% accuracy on that dataset. |
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The model takes a news article and predicts if it is true or fake. |
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The format of the input should be: |
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``` |
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<title> TITLE HERE <content> CONTENT HERE <end> |
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``` |
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## Using this model in your code |
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To use this model, first download it from the hugginface website: |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("hamzab/roberta-fake-news-classification") |
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model = AutoModelForSequenceClassification.from_pretrained("hamzab/roberta-fake-news-classification") |
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``` |
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Then, make a prediction like follows: |
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```python |
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import torch |
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def predict_fake(title,text): |
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input_str = "<title>" + title + "<content>" + text + "<end>" |
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input_ids = tokenizer.encode_plus(input_str, max_length=512, padding="max_length", truncation=True, return_tensors="pt") |
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device = 'cuda' if torch.cuda.is_available() else 'cpu' |
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model.to(device) |
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with torch.no_grad(): |
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output = model(input_ids["input_ids"].to(device), attention_mask=input_ids["attention_mask"].to(device)) |
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return dict(zip(["Fake","Real"], [x.item() for x in list(torch.nn.Softmax()(output.logits)[0])] )) |
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print(predict_fake(<HEADLINE-HERE>,<CONTENT-HERE>)) |
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``` |
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You can also use Gradio to test the model on real-time: |
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```python |
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import gradio as gr |
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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) |
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``` |
