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TrelBERT is a BERT-based Language Model trained on data from Polish Twitter using Masked Language Modeling objective. It is based on HerBERT model and therefore released under the same license - CC BY 4.0.


We trained our model starting from herbert-base-cased checkpoint and continued MLM training using data collected from Twitter.

The data we used for MLM fine-tuning was approximately 45 million Polish tweets. We trained the model for 1 epoch with a learning rate 5e-5 and batch size 2184 using AdamW optimizer.


For each Tweet, the user handles that occur in the beginning of the text were removed, as they are not part of the message content but only represent who the user is replying to. The remaining user handles were replaced by "@anonymized_account". Links were replaced with a special @URL token.


We use HerBERT tokenizer with two special tokens added for preprocessing purposes as described above (@anonymized_account, @URL). Maximum sequence length is set to 128, based on the analysis of Twitter data distribution.


CC BY 4.0

KLEJ Benchmark results

We fine-tuned TrelBERT to KLEJ benchmark tasks and achieved the following results:

Task name Score
CDSC-E 93.9
CDSC-R 93.6
CBD 76.1
PolEmo2.0-IN 89.3
PolEmo2.0-OUT 78.1
DYK 67.4
PSC 95.7
AR 86.1
Average 86.1

For fine-tuning to KLEJ tasks we used Polish RoBERTa scripts, which we modified to use transformers library. For the CBD task, we set the maximum sequence length to 128 and implemented the same preprocessing procedure as in the MLM phase.

Our model achieved 1st place in cyberbullying detection (CBD) task in the KLEJ leaderboard. Overall, it reached 7th place, just below HerBERT model.


Jakub Bartczuk, Krzysztof Dziedzic, Piotr Falkiewicz, Alicja Kotyla, Wojciech Szmyd, Michał Zobniów, Artur Zygadło

For more information, reach out to us via e-mail: artur.zygadlo@deepsense.ai

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