mikemcrae25/newswire_classifiers

Newswire Classifier (AP, UPI, NEA) - BERT Transformers

📘 Overview

This repository contains three separately trained BERT models for identifying whether a newspaper article was produced by one of three major newswire services:

• AP (Associated Press)
• UPI (United Press International)
• NEA (Newspaper Enterprise Association)

The models are designed for historical news classification from public-domain newswire articles (1960–1975).

🧠 Model Architecture

• Base Model: bert-base-uncased
• Task: Binary classification (1 if from the specific newswire, 0 otherwise)
• Optimizer: AdamW
• Loss Function: Binary Cross-Entropy with Logits
• Batch Size: 16
• Epochs: 4
• Learning Rate: 2e-5
• Device: TPU (v2-8) in Google Colab

📊 Training Data

• Source: Historical newspapers (1960–1975, public domain)
• Articles: 4000 per training round (1000 from target newswire, 3000 from other sources)
• Features Used: Headline, author, and first 100 characters of the article.
• Labeling: 1 for articles from the target newswire, 0 for all others.

🚀 Model Performance

Model	Accuracy	Precision	Recall	F1 Score
AP	0.9925	0.9926	0.9925	0.9925
UPI	0.9999	0.9999	0.9999	0.9999
NEA	0.9875	0.9880	0.9875	0.9876

🛠️ Usage

Installation

pip install transformers torch

Example Inference (AP Classifier)

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model = AutoModelForSequenceClassification.from_pretrained("mike-mcrae/newswire_classifier/AP")
tokenizer = AutoTokenizer.from_pretrained("mike-mcrae/newswire_classifier/AP")

text = "(AP) President speaks at conference..."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
outputs = model(**inputs)
prediction = outputs.logits.argmax().item()
print("AP Article" if prediction == 1 else "Not AP Article")

⚙️ Recommended Usage Notes

• The models were trained on a combination of the first 100 characters of headline + author + the first 100 characters of articles, as the mention of the newswire often appears in these sections. Using the same format for inference may improve accuracy.

📜 Licensing & Data Source

• Training Data: Historical newspaper articles (1960–1975) from public-domain sources.
• License: Public domain (for data) and MIT License (for model and code).

💬 Citation

If you use these models, please cite:

@misc{newswire_classifier,
  author = {McRae, Michael},
  title = {Newswire Classifier (AP, UPI, NEA) - BERT Transformers},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/username/newswire_classifier}
}