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language: pl
  - text-classification
  - sentiment-analysis
  - twitter
  - datasets/tweet_eval
  - f1
  - accuracy
  - precision
  - recall
  - text: Szczęście i Opatrzność mają znaczenie Gratuluje @pzpn_pl
    example_title: Example 1
  - text: >-
      Osoby z Ukrainy zapłacą za życie w centrach pomocy? Sprzeczne prawem UE,
      niehumanitarne, okrutne.
    example_title: Example 2
  - text: O której kończycie dzisiaj?
    example_title: Example 3

Twitter Sentiment PL (fast)

Twitter Sentiment PL (base) is a model based on distiluse for analyzing sentiment 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 three labels: positive, negative and neutral.

How to use

You can use this model directly with a pipeline for sentiment-analysis:

from transformers import pipeline

nlp = pipeline("sentiment-analysis", model="bardsai/twitter-sentiment-pl-fast")
nlp("Szczęście i Opatrzność mają znaczenie Gratuluje @pzpn_pl")
[{'label': 'positive', 'score': 0.9965680837631226}]


Metric Value
f1 macro 0.570
precision macro 0.570
recall macro 0.575
accuracy 0.582
samples per second 225.9

(The performance was evaluated on RTX 3090 gpu)


  • 2023-07-19: Initial release


At, 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:

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