Initial commit

.gitignore

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+venv

app.py

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+# Importing necessary libraries
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments, TextClassificationPipeline
+from sklearn.metrics import accuracy_score, precision_recall_fscore_support
+import gradio as gr
+
+# Load the dataset
+ds = load_dataset("GonzaloA/fake_news")
+
+# Load pre-trained tokenizer
+tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+
+# Define tokenization function
+def tokenize_function(examples):
+    return tokenizer(examples['text'], padding='max_length', truncation=True, max_length=128)
+
+# Apply tokenization
+tokenized_datasets = ds.map(tokenize_function, batched=True)
+
+# Load pre-trained BERT model for sequence classification
+model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)
+
+# Define training arguments
+training_args = TrainingArguments(
+    output_dir='./results',
+    num_train_epochs=3,
+    per_device_train_batch_size=8,
+    per_device_eval_batch_size=8,
+    evaluation_strategy='epoch',
+    logging_dir='./logs',
+)
+
+# Create trainer instance
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets['train'].shuffle().select(range(1000)),
+    eval_dataset=tokenized_datasets['test'].shuffle().select(range(1000)),
+)
+
+# Start training
+trainer.train()
+
+# Define function to compute metrics
+def compute_metrics(pred):
+    labels = pred.label_ids
+    preds = pred.predictions.argmax(-1)
+    precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='binary')
+    acc = accuracy_score(labels, preds)
+    return {'accuracy': acc, 'f1': f1, 'precision': precision, 'recall': recall}
+
+# Update trainer to include custom metrics
+trainer.compute_metrics = compute_metrics
+
+# Evaluate the model
+eval_result = trainer.evaluate()
+print(eval_result)
+
+# Save the fine-tuned model and tokenizer
+trainer.save_model('TeamQuad-fine-tuned-bert')
+tokenizer.save_pretrained('TeamQuad-fine-tuned-bert')
+
+# Load the fine-tuned model and tokenizer
+new_model = AutoModelForSequenceClassification.from_pretrained('TeamQuad-fine-tuned-bert')
+new_tokenizer = AutoTokenizer.from_pretrained('TeamQuad-fine-tuned-bert')
+
+# Create a classification pipeline
+classifier = TextClassificationPipeline(model=new_model, tokenizer=new_tokenizer)
+
+# Add label mapping for fake news detection (assuming LABEL_0 = 'fake' and LABEL_1 = 'true')
+label_mapping = {0: 'fake', 1: 'true'}
+
+# Function to classify input text
+def classify_news(text):
+    result = classifier(text)
+    # Extract the label and score
+    label = result[0]['label']  # 'LABEL_0' or 'LABEL_1'
+    score = result[0]['score']  # Confidence score
+    mapped_result = {'label': label_mapping[int(label.split('_')[1])], 'score': score}
+    return f"Label: {mapped_result['label']}, Score: {mapped_result['score']:.4f}"
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=classify_news,  # The function to process the input
+    inputs=gr.Textbox(lines=10, placeholder="Enter a news headline or article to classify..."),
+    outputs="text",  # Output will be displayed as text
+    title="Fake News Detection",
+    description="Enter a news headline or article and see whether the model classifies it as 'Fake News' or 'True News'.",
+)
+
+# Launch the interface
+iface.launch(share=True)

requirements.txt

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+transformers
+torch