Twitter emotion PL (base)

Twitter emotion PL (base) is a model based on herbert-base for analyzing emotion of Polish twitter posts. It was trained on the translated version of TweetEval by Barbieri et al., 2020 for 10 epochs on single RTX3090 gpu.

The model will give you a four labels: joy, optimism, sadness and anger.

How to use

You can use this model directly with a pipeline for text classification:

from transformers import pipeline

nlp = pipeline("text-classification", model="bardsai/twitter-emotion-pl-base")
nlp("Nigdy przegrana nie sprawiła mi takiej radości. Szczęście i Opatrzność mają znaczenie Gratuluje @pzpn_pl")
[{'label': 'joy', 'score': 0.5163766145706177}]

Performance

Metric Value
f1 macro 0.756
precision macro 0.767
recall macro 0.750
accuracy 0.789
samples per second 131.6

(The performance was evaluated on RTX 3090 gpu)

Changelog

  • 2023-07-19: Initial release

License

This model is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, inherited from the base model allegro/herbert-base-cased (also CC BY 4.0).

Attribution: HerBERT — Allegro ML Research and the Linguistic Engineering Group at the Institute of Computer Science, Polish Academy of Sciences; Twitter emotion PL (base) — bards.ai.

About bards.ai

At bards.ai, we focus on providing machine learning expertise and skills to our partners, particularly in the areas of nlp, machine vision and time series analysis. Our team is located in Wroclaw, Poland. Please visit our website for more information: bards.ai

Let us know if you use our model :). Also, if you need any help, feel free to contact us at info@bards.ai

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Evaluation results

  • F1 (macro) on TweetEval (translated to Polish)
    self-reported
    0.756
  • Precision (macro) on TweetEval (translated to Polish)
    self-reported
    0.767
  • Recall (macro) on TweetEval (translated to Polish)
    self-reported
    0.750
  • Accuracy on TweetEval (translated to Polish)
    self-reported
    0.789