Fast Twitter Emotion Detection
Twitter Emotion PL (base) is a Polish-language emotion classification model built on top of herbert-base. It distinguishes between four emotions commonly found in social media: joy, optimism, sadness, and anger.
Trained on a translated version of the TweetEval dataset, it delivers strong performance across all key metrics (F1 macro: 0.756, accuracy: 0.789) and processes over 130 tweets per second on a single RTX 3090 (base version).
This model is well-suited for applications in media monitoring, opinion analysis, and research on online discourse in Polish.
How to use
from transformers import pipeline
nlp = pipeline("text-classification", model="bardsai/twitter-emotion-pl-base")
nlp("Nigdy przegrana nie sprawila mi takiej radosci."){'label': 'joy', 'score': 0.5163766145706177}Performance Metrics
| Metric | Value |
|---|---|
| F1 Macro | 0.756 |
| Precision | 0.767 |
| Recall | 0.750 |
| Accuracy | 0.789 |
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