ringorsolya
/

GuiltRoBERTa-hu

@@ -1,199 +1,122 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-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
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+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)