---
language:
- hu
license: mit
tags:
- text-classification
- emotion-detection
- machine-learning
- hungarian
model_name: poltextlab/xlm-roberta-large-pooled-hungarian-emotions9-v2
datasets: []
metrics:
- precision
- recall
- f1-score
- accuracy
extra_gated_fields:
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  Country: country
  Institution: text
  Institution Email: text
  Please specify your academic use case: text
extra_gated_prompt: Our models are intended for academic use only. If you are not
  affiliated with an academic institution, please provide a rationale for using our
  models. Please allow us a few business days to manually review subscriptions.
---

# poltextlab/xlm-roberta-large-pooled-hungarian-emotions9-v2
### Proposal 2B: German to Hungarian Emotion Labeling (v2)

## Model Description

This model is designed for **emotion classification** in Hungarian texts that have been translated from German.  
It was fine-tuned to recognize **nine emotion categories** and trained on a dataset with labeled examples.

## Labels and Their Meanings

| Label | Emotion      |
|--------|----------------|
| 0      | Anger        |
| 1      | Fear         |
| 2      | Disgust      |
| 3      | Sadness      |
| 4      | Joy          |
| 5      | None of them |
| 6      | Enthusiasm   |
| 7      | Hope         |
| 8      | Pride        |

## Evaluation Metrics

The model was evaluated using **precision, recall, f1-score, and accuracy**.

### Classification Report

| Label         | Precision | Recall | F1-score | Support |
|---------------|-----------|--------|----------|---------|
| Anger (0)     | 0.52      | 0.58   | 0.55     | 777     |
| Fear (1)      | 0.86      | 0.75   | 0.80     | 776     |
| Disgust (2)   | 0.94      | 0.94   | 0.94     | 776     |
| Sadness (3)   | 0.86      | 0.85   | 0.86     | 775     |
| Joy (4)       | 0.85      | 0.77   | 0.81     | 736     |
| None of them (5) | 0.67   | 0.62   | 0.64     | 1594    |
| Enthusiasm (6)| 0.65      | 0.61   | 0.63     | 776     |
| Hope (7)      | 0.48      | 0.60   | 0.53     | 777     |
| Pride (8)     | 0.75      | 0.81   | 0.78     | 776     |

#### Overall Performance:

- **Accuracy**: 71%
- **Macro Avg**: Precision: 0.73, Recall: 0.73, F1-score: 0.73
- **Weighted Avg**: Precision: 0.72, Recall: 0.71, F1-score: 0.72

## How to Use

To use this model for text classification in Python:

```python
from transformers import pipeline

classifier = pipeline("text-classification", model="poltextlab/xlm-roberta-large-pooled-hungarian-emotions9-v2", use_auth_token="<HF_TOKEN>")

text = "Nagyon örülök, hogy itt vagy!" 
result = classifier(text)
print(result)
```