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You're reading from  Mastering Transformers

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Published inSep 2021
PublisherPackt
ISBN-139781801077651
Edition1st Edition
Tools
TensorFlow
Concepts
Mobile Application Development
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Savaş Yıldırım
Savaş Yıldırım
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Savaş Yıldırım

Savaş Yıldırım graduated from the Istanbul Technical University Department of Computer Engineering and holds a Ph.D. degree in Natural Language Processing (NLP). Currently, he is an associate professor at the Istanbul Bilgi University, Turkey, and is a visiting researcher at the Ryerson University, Canada. He is a proactive lecturer and researcher with more than 20 years of experience teaching courses on machine learning, deep learning, and NLP. He has significantly contributed to the Turkish NLP community by developing a lot of open source software and resources. He also provides comprehensive consultancy to AI companies on their R&D projects. In his spare time, he writes and directs short films, and enjoys practicing yoga.
Read more about Savaş Yıldırım

Meysam Asgari- Chenaghlu
Meysam Asgari- Chenaghlu
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Meysam Asgari- Chenaghlu

Meysam Asgari-Chenaghlu is an AI manager at Carbon Consulting and is also a Ph.D. candidate at the University of Tabriz. He has been a consultant for Turkey's leading telecommunication and banking companies. He has also worked on various projects, including natural language understanding and semantic search.
Read more about Meysam Asgari- Chenaghlu

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Autoencoding language model training for any language

We have discussed how BERT works and that it is possible to use the pretrained version of it provided by the HuggingFace repository. In this section, you will learn how to use the HuggingFace library to train your own BERT.

Before we start, it is essential to have good training data, which will be used for the language modeling. This data is called the corpus, which is normally a huge pile of data (sometimes it is preprocessed and cleaned). This unlabeled corpus must be appropriate for the use case you wish to have your language model trained on; for example, if you are trying to have a special BERT for, let's say, the English language. Although there are tons of huge, good datasets, such as Common Crawl (https://commoncrawl.org/), we would prefer a small one for faster training.

The IMDB dataset of 50K movie reviews (available at https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews) is a large dataset...

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Mastering Transformers
Published in: Sep 2021Publisher: PacktISBN-13: 9781801077651
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Authors (2)

author image
Savaş Yıldırım

Savaş Yıldırım graduated from the Istanbul Technical University Department of Computer Engineering and holds a Ph.D. degree in Natural Language Processing (NLP). Currently, he is an associate professor at the Istanbul Bilgi University, Turkey, and is a visiting researcher at the Ryerson University, Canada. He is a proactive lecturer and researcher with more than 20 years of experience teaching courses on machine learning, deep learning, and NLP. He has significantly contributed to the Turkish NLP community by developing a lot of open source software and resources. He also provides comprehensive consultancy to AI companies on their R&D projects. In his spare time, he writes and directs short films, and enjoys practicing yoga.
Read more about Savaş Yıldırım

author image
Meysam Asgari- Chenaghlu

Meysam Asgari-Chenaghlu is an AI manager at Carbon Consulting and is also a Ph.D. candidate at the University of Tabriz. He has been a consultant for Turkey's leading telecommunication and banking companies. He has also worked on various projects, including natural language understanding and semantic search.
Read more about Meysam Asgari- Chenaghlu

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