political-lean-classifier (best_bias_model)

Fine-tuned roberta-base model for political bias score prediction from news text.

This checkpoint is trained as a regression model (num_labels=1) and outputs a single continuous score. In this project, target labels are derived from the bias field of the dataset and commonly fall in the 0 to 4 range, indicating leaning from "extreme left" to "extreme right".

Model Details

Architecture: RobertaForSequenceClassification
Base model: roberta-base
Task type: text regression
Max sequence length: 512
Language: English

Training Data

Dataset: pietrolesci/hyperpartisan_news_detection
Split used: sampled subset of train split
Rows used in this project: 50,000
Input text: concatenated article title + cleaned article body
Label: numeric bias score (bias)

Intended Use

Use this model to estimate political bias tendency of English news text at a document level.

Potential use cases:

Media analysis dashboards
Content trend analysis
Research experiments on bias scoring

This model is not intended to be used as the sole basis for moderation, ranking, or policy decisions.

Quick Start

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_id = "zhezhou1106/political-leaning-classifier"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
model.eval()

text = "Tax cuts for corporations will result in increased economic activity."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)

with torch.no_grad():
    score = model(**inputs).logits.squeeze().item()

print("Predicted bias score:", score)