---
license: cc-by-4.0
language:
- en
metrics:
- accuracy
base_model: facebook/roberta-large
pipeline_tag: text-classification
tags:
- framing
---

# Sentence Frame Classifier

A RoBERTa-based model for detecting media frames at the sentence level. This model can classify sentences into 10 different frame categories and works across both news articles and reader comments.

## Model Description

This model was trained to identify media frames in text at the sentence level. It's based on the Media Frame Corpus [*Card et al. 2015*](https://aclanthology.org/P15-2072.pdf) and extends to online discussion contexts [*Hartmann et al., 2019*](https://arxiv.org/pdf/1904.03969), making it suitable for analyzing both professional journalism and user-generated content.

**Key Features:**

- Sentence-level frame classification
- Cross-domain capability (news articles + comments)
- 9 frame categories based on established political communication theory
- Robust performance across different topics

## Frame Categories

The model classifies sentences into these 9 frame categories:

- Economic — Economic costs, benefits, or implications
- Morality — Moral or ethical considerations
- Fairness and Equality — Issues of fairness, equality, or discrimination
- Legality and Crime — Legal aspects, constitutionality, crime, and punishment
- Political and Policies — Political processes, policy prescriptions, and evaluations
- Security and Defense — Security threats, defense, or public safety
- Health and Safety — Health risks, safety concerns, or medical implications
- Cultural Identity — Cultural values, traditions, or identity issues
- Public Opinion — Public sentiment, polls, or popular support
- Other/None

## Performance

- Macro F1: 0.66
- Accuracy: 0.77
- Cross-topic generalization: Robust performance across different topics
- Validation: Human-validated on 600 sentences

## Usage

```python
from transformers import pipeline

classifier = pipeline("text-classification", model="mattdr/cross-domain-frame-classifier")

text = "The new policy will cost taxpayers millions of dollars while providing few benefits."
result = classifier(text)
print(result)
# [{'label': 'Economic', 'score': 0.89}]

examples = [
    "The economy of the country is improving steadily.",
    "The public strongly supports this initiative according to recent polls.",
    "We must protect our children from these dangerous substances."
]

for text in examples:
    result = classifier(text)
    print(f"Text: {text}")
    print(f"Frame: {result[0]['label']} (confidence: {result[0]['score']:.2f})")
    print()