metadata
title: News Source Classifier
emoji: 📰
colorFrom: blue
colorTo: red
sdk: streamlit
app_file: eval_pipeline.py
library_name: transformers
pinned: false
language: en
license: mit
tags:
- text-classification
- news-classification
- BERT
- pytorch
- transformers
pipeline_tag: text-classification
widget:
- example_title: Politics News Headline
text: Trump's campaign rival decides between voting for him or Biden
- example_title: International News Headline
text: >-
World Food Programme Director Cindy McCain: Northern Gaza is in a
'full-blown famine'
- example_title: Domestic News Headline
text: >-
Ohio sheriff suggests residents keep a list of homes with Harris yard
signs
model-index:
- name: News Source Classifier
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Custom FOX-NBC Dataset
type: Custom
metrics:
- name: F1 Score
type: f1
value: 0.85
News Source Classifier - BERT Model
Model Overview
This repository contains a fine-tuned BERT model that classifies news headlines between Fox News and NBC News, along with an evaluation pipeline for assessing model performance using Streamlit.
Model Details
- Base Model: BERT (bert-base-uncased)
- Task: Binary classification (Fox News vs NBC News)
- Model ID: CIS519PG/News_Classifier_Demo
- Training Data: News headlines from Fox News and NBC News
- Input: News article headlines (text)
- Output: Binary classification with probability scores
Evaluation Pipeline Setup
Prerequisites
- Python 3.8+
- pip package manager
Required Dependencies
Install the required packages using pip:
pip install streamlit pandas torch transformers scikit-learn numpy plotly tqdm
Running the Evaluation Pipeline
- Save the following provided evaluation code as
eval_pipeline.py, also downloadable in files.
import streamlit as st
import pandas as pd
import torch
from transformers import BertTokenizer, AutoModelForSequenceClassification
from sklearn.metrics import roc_auc_score, roc_curve, confusion_matrix, classification_report, f1_score, precision_recall_fscore_support
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
from tqdm import tqdm
def load_model_and_tokenizer():
try:
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("CIS519PG/News_Classifier_Demo")
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
model.eval()
return model, tokenizer, device
except Exception as e:
st.error(f"Error loading model or tokenizer: {str(e)}")
return None, None, None
def preprocess_data(df):
try:
processed_data = []
for _, row in df.iterrows():
outlet = row["outlet"].strip().upper()
if outlet == "FOX NEWS":
outlet = "FOXNEWS"
elif outlet == "NBC NEWS":
outlet = "NBC"
processed_data.append({
"title": row["title"],
"outlet": outlet
})
return processed_data
except Exception as e:
st.error(f"Error preprocessing data: {str(e)}")
return None
def evaluate_model(model, tokenizer, device, test_dataset):
label2id = {"FOXNEWS": 0, "NBC": 1}
all_logits = []
references = []
batch_size = 16
progress_bar = st.progress(0)
for i in range(0, len(test_dataset), batch_size):
progress = min(i / len(test_dataset), 1.0)
progress_bar.progress(progress)
batch = test_dataset[i:i + batch_size]
texts = [item['title'] for item in batch]
encoded = tokenizer(
texts,
padding=True,
truncation=True,
max_length=128,
return_tensors="pt"
)
inputs = {k: v.to(device) for k, v in encoded.items()}
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits.cpu().numpy()
true_labels = [label2id[item['outlet']] for item in batch]
all_logits.extend(logits)
references.extend(true_labels)
progress_bar.progress(1.0)
probabilities = torch.softmax(torch.tensor(all_logits), dim=1).numpy()
return references, probabilities
def plot_roc_curve(references, probabilities):
fpr, tpr, _ = roc_curve(references, probabilities[:, 1])
auc_score = roc_auc_score(references, probabilities[:, 1])
fig = go.Figure()
fig.add_trace(go.Scatter(x=fpr, y=tpr, name=f'ROC Curve (AUC = {auc_score:.4f})'))
fig.add_trace(go.Scatter(x=[0, 1], y=[0, 1], name='Random Guess', line=dict(dash='dash')))
fig.update_layout(
title='ROC Curve',
xaxis_title='False Positive Rate',
yaxis_title='True Positive Rate',
showlegend=True
)
return fig, auc_score
def plot_metrics_by_threshold(references, probabilities):
thresholds = np.arange(0.0, 1.0, 0.01)
metrics = {
'threshold': thresholds,
'f1': [],
'precision': [],
'recall': []
}
best_f1 = 0
best_threshold = 0
best_metrics = {}
for threshold in thresholds:
preds = (probabilities[:, 1] > threshold).astype(int)
f1 = f1_score(references, preds)
precision, recall, _, _ = precision_recall_fscore_support(references, preds, average='binary')
metrics['f1'].append(f1)
metrics['precision'].append(precision)
metrics['recall'].append(recall)
if f1 > best_f1:
best_f1 = f1
best_threshold = threshold
cm = confusion_matrix(references, preds)
report = classification_report(references, preds, target_names=['FOXNEWS', 'NBC'], digits=4)
best_metrics = {
'threshold': threshold,
'f1_score': f1,
'confusion_matrix': cm,
'classification_report': report
}
fig = go.Figure()
fig.add_trace(go.Scatter(x=thresholds, y=metrics['f1'], name='F1 Score'))
fig.add_trace(go.Scatter(x=thresholds, y=metrics['precision'], name='Precision'))
fig.add_trace(go.Scatter(x=thresholds, y=metrics['recall'], name='Recall'))
fig.update_layout(
title='Metrics by Threshold',
xaxis_title='Threshold',
yaxis_title='Score',
showlegend=True
)
return fig, best_metrics
def plot_confusion_matrix(cm):
labels = ['FOXNEWS', 'NBC']
annotations = []
for i in range(len(labels)):
for j in range(len(labels)):
annotations.append(
dict(
text=str(cm[i, j]),
x=labels[j],
y=labels[i],
showarrow=False,
font=dict(color='white' if cm[i, j] > cm.max()/2 else 'black')
)
)
fig = go.Figure(data=go.Heatmap(
z=cm,
x=labels,
y=labels,
colorscale='Blues',
showscale=True
))
fig.update_layout(
title='Confusion Matrix',
xaxis_title='Predicted Label',
yaxis_title='True Label',
annotations=annotations
)
return fig
def main():
st.title("News Classifier Model Evaluation")
uploaded_file = st.file_uploader("Upload your test dataset (CSV)", type=['csv'])
if uploaded_file is not None:
df = pd.read_csv(uploaded_file)
st.write("Preview of uploaded data:")
st.dataframe(df.head())
model, tokenizer, device = load_model_and_tokenizer()
if model and tokenizer:
test_dataset = preprocess_data(df)
if test_dataset:
st.write(f"Total examples: {len(test_dataset)}")
with st.spinner('Evaluating model...'):
references, probabilities = evaluate_model(model, tokenizer, device, test_dataset)
roc_fig, auc_score = plot_roc_curve(references, probabilities)
st.plotly_chart(roc_fig)
st.metric("AUC-ROC Score", f"{auc_score:.4f}")
metrics_fig, best_metrics = plot_metrics_by_threshold(references, probabilities)
st.plotly_chart(metrics_fig)
st.subheader("Best Threshold Evaluation")
col1, col2 = st.columns(2)
with col1:
st.metric("Best Threshold", f"{best_metrics['threshold']:.2f}")
with col2:
st.metric("Best F1 Score", f"{best_metrics['f1_score']:.4f}")
st.subheader("Confusion Matrix")
cm_fig = plot_confusion_matrix(best_metrics['confusion_matrix'])
st.plotly_chart(cm_fig)
st.subheader("Classification Report")
st.text(best_metrics['classification_report'])
if __name__ == "__main__":
main()
- Run the Streamlit application:
streamlit run eval_pipeline.py
- The web interface will automatically open in your default browser
Using the Web Interface
Upload Test Data:
- Prepare your test data in CSV format
- Required columns:
- Index column (automatic numbering)
- "title": The news headline text
- "label": Binary label (0 for Fox News, 1 for NBC News)
- "News Outlet": The source ("Fox News" or "NBC News")
View Evaluation Results: The pipeline will display:
- Data preview
- ROC curve with AUC score
- Metrics vs threshold plot
- Best threshold and F1 score
- Confusion matrix visualization
- Detailed classification report
Sample Data Format
,title,label,News Outlet
0,"Jack Carr's take on the late Tom Clancy, born on this day in 1947",0,Fox News
1,"Feeding America CEO asks community to help others amid today's high inflation",0,Fox News
2,"World Food Programme Director Cindy McCain: Northern Gaza is in a 'full-blown famine'",1,NBC News
3,"Ohio sheriff suggests residents keep a list of homes with Harris yard signs",1,NBC News
Model Architecture
- Base model: BERT (bert-base-uncased)
- Fine-tuned for binary classification
- Uses PyTorch and Hugging Face Transformers
Limitations and Bias
This model has been trained on news headlines from specific sources (Fox News and NBC News) and time periods, which may introduce certain biases:
- Limited to two specific news sources
- Temporal bias based on training data collection period
- May not generalize well to other news sources or formats
Evaluation Metrics
The pipeline provides comprehensive evaluation metrics:
- AUC-ROC Score
- F1 Score
- Precision & Recall
- Confusion Matrix
- Detailed Classification Report
Troubleshooting
Common issues and solutions:
CUDA/GPU Error:
- The pipeline automatically falls back to CPU if CUDA is not available
- No action needed from user
Memory Issues:
- Default batch size is 16
- Reduce batch size if memory constraints exist
File Format Error:
- Ensure CSV file has exact column names: "title", "label", "News Outlet"
- Verify label values are 0 or 1
- Confirm "News Outlet" values are exactly "Fox News" or "NBC News"
License
This project is licensed under the MIT License.