---
language:
  - hu
license: cc-by-nc-4.0
library_name: transformers
tags:
  - hungarian
  - xlm-roberta
  - text-classification
  - guilt-assignment
  - political-communication
  - two-stage-model
  - emotion-filtering
  - babel-emotions
  - research
datasets:
  - "guilt_roberta_train.xlsx"
  - "gold_standard_with_emotion_prediction.xlsx"
task_categories:
  - text-classification
metrics:
  - precision
  - recall
  - f1
model-index:
  - name: GuiltRoBERTa-hu
    results:
      - task:
          type: text-classification
          name: Guilt Assignment Detection (Two-Stage, Hungarian)
        dataset:
          name: Independent Gold Standard (Hungarian Political Discourse)
          type: custom
          split: test
        metrics:
          - type: precision
            value: 0.78
          - type: recall
            value: 0.95
          - type: f1
            value: 0.85
---


# 🇭🇺 GuiltRoBERTa-hu: A Two-Stage Classifier for Guilt-Assignment Rhetoric in Hungarian Political Texts

**GuiltRoBERTa-hu** is a **two-stage AI pipeline** for detecting **guilt-assignment rhetoric** in Hungarian political discourse.  
It combines:

1. **Stage 1 – Emotion Pre-Filtering:** emotion labels from the [Babel Emotions Tool](https://emotionsbabel.poltextlab.com/),  
2. **Stage 2 – Guilt Classification:** a fine-tuned binary XLM-RoBERTa model trained on manually annotated Hungarian texts (`guilt` vs `no_guilt`).

The approach is grounded in political communication theory, which suggests that **guilt attribution often emerges in anger-laden contexts**.  
Thus, only **texts labeled as “Anger”** in Stage 1 are passed to the guilt classifier.

---

## 🧩 Model Architecture

### Stage 1: Emotion Pre-Filtering (Babel Emotions Tool)
- **Tool:** [Emotions 6 Babel Machine](https://emotionsbabel.poltextlab.com/) (developed by PoltextLAB)
- **Task:** 6-class emotion classification (`Anger`, `Fear`, `Disgust`, `Sadness`, `Joy`, `None`)
- **Input:** CSV file with one text per row  
- **Output:** CSV file with predicted labels and probabilities  
- **Usage:** retain only rows with `predicted_emotion == "Anger"` for Stage 2

> ⚙️ The Babel Emotions Tool is **not an API** but a **web-based interface**.  
> Upload a CSV file, download the labeled results, and use them as input to the guilt classifier.  
> The included notebook loads these predictions from `gold_standard_with_emotion_prediction.xlsx`.

### Stage 2: Guilt Classification
- **Base model:** `xlm-roberta-base`
- **Task:** Binary classification (`guilt`, `no_guilt`)
- **Training data:** `guilt_roberta_train.xlsx`
- **Evaluation:** Independent gold-standard dataset of Hungarian political discourse

---

## 🧠 Motivation

Guilt assignment — attributing moral responsibility or blame — is a key rhetorical strategy in political communication.  
Since guilt often appears alongside anger, **direct one-stage classification** risks conflating emotional tones.  

This two-stage pipeline improves precision by:
- Filtering **anger-related** contexts first  
- Then applying a **dedicated guilt detector** only where relevant

---

## 📊 Evaluation (Gold Standard)

| Stage 1 Filter | Threshold (τ) | Precision | Recall | F1  | Accuracy |
|----------------|----------------|------------|--------|-----|-----------|
| Anger-only     | 0.01           | 0.78       | 0.95   | 0.85 | 0.75     |

> Best configuration: **Anger-only**, τ = 0.01  
> ROC-AUC = 0.61 PR-AUC = 0.82  
> The two-stage model improves F1 by ≈ +0.20 compared to single-stage baselines.

---

## 🚀 Usage Example

```python
import pandas as pd
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline

# Load Babel emotion predictions
df = pd.read_excel("gold_standard_with_emotion_prediction.xlsx")

# Filter for 'Anger'
anger_df = df[df["emotion_predicted"] == "Anger"].copy()

# Load the guilt classifier
repo_id = "<your-org>/guiltroberta-hu"
tok = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(repo_id)
pipe = TextClassificationPipeline(model=model, tokenizer=tok, return_all_scores=True)

# Apply predictions
anger_df["guilt_score"] = anger_df["text"].apply(lambda t: pipe(t)[0][1]["score"])  # score for 'guilt'
anger_df.to_excel("anger_with_guilt_predictions.xlsx", index=False)