## 5 Pretraining a Transformer-Based Language Model for Greek  
Large pretrained language models (PLMs) based on the transformer architecture [40] have been repeatedly shown to outperform other approaches in a variety of tasks [36]. These models are trained on huge corpora using self-supervised learning, i.e., the models are used to directly learn the structure of the language in the corpus through some form of language modelling rather than being trained to solve a supervised task. Regarding the Greek language, publicly available resources are more limited than in English. Efforts have been made in the literature to develop a Greek version of BERT [19], which is a widely used and useful resource. One problem is that, due to the limited number of public resources, Greek BERT training is by and large based on the OSCAR dataset [87], which is a filtered and deduplicated, but still noisy, version of the Common Crawl corpus ([[https://commoncrawl.org/](https://commoncrawl.org/)]([https://commoncrawl.org/](https://commoncrawl.org/)), accessed on 31 May 2022). For this reason, we aim to construct a better pretraining corpus for training large Greek PLMs.  
Our model is based on the ELECTRA architecture [18]. Specifically, we trained a similarly sized variant, with 12 layers and 768 hidden size (electro-base). ELECTRA is trained as a discriminator that tries to detect token replacements produced by a small MLM generator. We base the training corpus on the C4 dataset [88] instead of OSCAR. C4 is also based on the Common Crawl corpus, but includes additional filtering and cleaning steps:  
* The filtering of lines that do not end in punctuation, contain fewer than 3 words and/or contain obscene words.
* The removal of all pages that contain fewer than 5 sentences.
* The removal of Javascript and pages that contain code or placeholder tokens (e.g., \"lorem ipsum\").
* The deduplication of any three consecutive sentence spans that appear multiple times. In addition, we implemented a set of more aggressive cleaning steps to create the final version of the corpus, which we name Gfc4 (Greek filtered C4):
* The removal of lines that contain URLs.
* The removal of lines that do not contain Greek characters.
* The removal of lines that contain UTF-8 characters that do not belong in one of the following sets: (a) Greek, (b) Latin, (c) numeric, (d) punctuation, (e) accents.
* The removal of Ancient or purist Greek text, by removing lines that contain diacritics that are only used in Ancient or purist Greek (polytonic).
* We observed that, in many sentences in the corpus, some words were merged due to the crawling of the web pages (e.g., \"somewordsaremerged\"). We believe this should not be a large issue, since words are going to be split into subwords using a wordpiece algorithm [89]. Nevertheless, in order to produce the cleanest possible version of the corpus, we found words that were longer than 20 characters and split them using a bigram language model.  
To create the complete training corpus, we merged the Gfc4 corpus with the Greek version of Wikipedia and the Hellenic National Corpus (HNC) [90]. We also removed accents and converted all words to lower-case. The model was trained using AdamW optimizer [91] with a learning rate of \(10^{-4}\) and warmup of 8000 training steps, and batch size of 16. Due to limitations in computational resources, we used a 20 GB subset of the training corpus and trained the model on four NVIDIA RTX 3090 GPUs for 18 days.