README.md

---
license: mit
language:
- en
metrics:
- accuracy
---
# Model Card for POLLCHECK/RoBERTa-classifier
## Model Description
This RoBERTa model has been fine-tuned for a binary classification task to determine whether statements are 0 OR "biased/ fake" or 1 OR "unbiased/ real". The model is based on the RoBERTa architecture, a robustly optimized BERT pretraining approach.

## Intended Use
Primary Use: This model is intended for the classification of textual statements into two categories: biased and unbiased. It is suitable for analyzing news articles, editorials, and opinion pieces.
Users: This model can be used by data scientists, journalists, content moderators, and social media platforms to detect bias in text.
## Model Details
Architecture: The model uses the RoBERTa-base architecture.
Training Data: The model was trained on a curated dataset comprising news articles, editorials, and opinion pieces labeled as biased or unbiased by domain experts.
Performance Metrics

## Usage

- [Sample News Bias Dataset (CSV)](https://huggingface.co/POLLCHECK/RoBERTa-classifier/blob/main/News_Bias_Samples.csv)
- [Inference Script for RoBERTa Classifier (Python)](https://huggingface.co/POLLCHECK/RoBERTa-classifier/blob/main/inference-roberta.py)

```from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "POLLCHECK/RoBERTa-classifier"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

texts = [
    "Religious Extremists Threaten Our Way of Life.",
    "Public Health Officials are working."
]
for text in texts:
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    with torch.no_grad():
        outputs = model(**inputs)
        probabilities = torch.softmax(outputs.logits, dim=-1)
        predicted_label = "biased" if probabilities[0][0] > 0.5 else "unbiased"
        print(f"Text: {text}\nPredicted label: {predicted_label}")
```

## Results

The following table presents the evaluation metrics for each class along with macro averages:

| Class              | Precision | Recall | F1-Score |
|--------------------|-----------|--------|----------|
| Biased/ fake (0)   | 0.93      | 0.96   | 0.94     |
| Unbiased/ real (1) | 0.96      | 0.92   | 0.94     |
| Macro Avg          | 0.94      | 0.94   | 0.94     |