--- language: pl tags: - T5 - translation - summarization - question answering - reading comprehension datasets: - ccnet - nkjp - wikipedia - open subtitles - free readings license: cc-by-4.0 --- # plT5 Large **plT5** models are T5-based language models trained on Polish corpora. The models were optimized for the original T5 denoising target. ## Corpus plT5 was trained on six different corpora available for Polish language: | Corpus | Tokens | Documents | | :------ | ------: | ------: | | [CCNet Middle](https://github.com/facebookresearch/cc_net) | 3243M | 7.9M | | [CCNet Head](https://github.com/facebookresearch/cc_net) | 2641M | 7.0M | | [National Corpus of Polish](http://nkjp.pl/index.php?page=14&lang=1)| 1357M | 3.9M | | [Open Subtitles](http://opus.nlpl.eu/OpenSubtitles-v2018.php) | 1056M | 1.1M | [Wikipedia](https://dumps.wikimedia.org/) | 260M | 1.4M | | [Wolne Lektury](https://wolnelektury.pl/) | 41M | 5.5k | ## Tokenizer The training dataset was tokenized into subwords using a sentencepiece unigram model with vocabulary size of 50k tokens. ## Usage Example code: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("allegro/plt5-large") model = AutoModel.from_pretrained("allegro/plt5-large") ``` ## License CC BY 4.0 ## Citation If you use this model, please cite the following paper: ``` @article{chrabrowa2022evaluation, title={Evaluation of Transfer Learning for Polish with a Text-to-Text Model}, author={Chrabrowa, Aleksandra and Dragan, {\L}ukasz and Grzegorczyk, Karol and Kajtoch, Dariusz and Koszowski, Miko{\l}aj and Mroczkowski, Robert and Rybak, Piotr}, journal={arXiv preprint arXiv:2205.08808}, year={2022} } ``` ## Authors The model was trained by [**Machine Learning Research Team at Allegro**](https://ml.allegro.tech/) and [**Linguistic Engineering Group at Institute of Computer Science, Polish Academy of Sciences**](http://zil.ipipan.waw.pl/). You can contact us at: klejbenchmark@allegro.pl