peekayitachi/roberta-political-bias

Model Card for Model ID

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library_name: transformers license: mit tags: - roberta - text-classification - political-bias - transformers - nlp - fine-tuned datasets: - pranjali97/Bias-detection-combined - peekayitachi/allsides - custom-political-bias-data

🧠 RoBERTa Political Bias Classifier

This is a fine-tuned RoBERTa model for political bias detection in text. It classifies a sentence or article snippet into one of the following three categories:

• 🔴 Right
• 🟡 Center
• 🔵 Left

Trained on a combination of public and custom-labeled datasets, the model is capable of classifying political leaning in Indian and general English news/opinion text.

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📥 Example Usage

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model = AutoModelForSequenceClassification.from_pretrained("peekayitachi/roberta-political-bias")
tokenizer = AutoTokenizer.from_pretrained("peekayitachi/roberta-political-bias")

text = "Our nation's sovereignty must be protected, and we should prioritize national interests."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
    logits = model(**inputs).logits
    predicted = torch.argmax(logits, dim=1).item()

label_map = {0: "Left", 1: "Center", 2: "Right"}
print("Predicted Bias:", label_map[predicted])




## Model Details

### Model Description

Base model: roberta-base

Architecture: Transformer encoder with classification head

Fine-tuned on: Multi-source labeled data (~38k samples)

Languages: English (Indian and global political context)

License: MIT

Author: peekayitachi (Pranav)

## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

This model reflects the labeling choices and distribution of the training data. It may:

Overfit to news-style text and miss subtle bias in blogs/social media

Be less accurate on texts that are neutral in tone or multi-opinionated

Reflect U.S./Indian-centric definitions of political categories



### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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### Model Architecture and Objective

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### Compute Infrastructure

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