poltextlab / xlm-roberta-large-pooled-polish-emotions9-v2

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Total runs: 1.4K
24-hour runs: 0
7-day runs: 8
30-day runs: 187
Model's Last Updated: July 18 2025
text-classification

Introduction of xlm-roberta-large-pooled-polish-emotions9-v2

Model Details of xlm-roberta-large-pooled-polish-emotions9-v2

Proposal 2B: German to Slovak Emotion Labeling (v2)

Model Description

This model is designed for emotion classification in Slovak texts.
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) 0.54 0.56 0.55 777
Fear (1) 0.84 0.77 0.80 776
Disgust (2) 0.92 0.95 0.93 776
Sadness (3) 0.84 0.84 0.84 775
Joy (4) 0.83 0.79 0.81 736
None of them (5) 0.67 0.65 0.66 1594
Enthusiasm (6) 0.61 0.64 0.63 776
Hope (7) 0.51 0.54 0.53 777
Pride (8) 0.78 0.80 0.79 776
Overall Performance:
  • Accuracy : 72%
  • Macro Avg : Precision: 0.73, Recall: 0.73, F1-score: 0.73
  • Weighted Avg : Precision: 0.72, Recall: 0.72, F1-score: 0.72
How to Use

To use this model for text classification in Python:

from transformers import pipeline

classifier = pipeline("text-classification", model="poltextlab/proposal_2b_german_to_slovak_PT_label_v2")

text = "Ich bin sehr glücklich, dass du hier bist!"  # German
result = classifier(text)
print(result)

Runs of poltextlab xlm-roberta-large-pooled-polish-emotions9-v2 on huggingface.co

1.4K
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3-day runs
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7-day runs
187
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