Mastering Transformer Data Preparation: From Raw Text to Model-Ready Input
🚀 Mastering Data Preparation for Transformer Training 🚀
Embark on a guided journey into the intricate world of data preparation for training Transformers! In this tutorial, we will explore the critical steps required to transform raw text data into a format suitable for training a Transformer model, with a focus on sequence-to-sequence tasks.
Table of Content:
🎯 Set the Stage: Introduction to Data Preparation for Transformers.
🔧 Crafting Custom Tokenization: Building a Flexible CustomTokenizer Class.
📚 Creating Language-Specific Tokenizers: Spanish and English Instances.
🔗 Establishing a Data Pipeline: Utilizing the DataProvider Class.
Prepare to unlock the power of data manipulation for Transformer training. As we delve into creating custom tokenizers, organizing data pipelines, and streamlining preprocessing steps, you'll gain the tools to set your NLP projects on a path to success. Join us on this empowering journey as we transform raw text into meaningful training input, propelling your NLP endeavors to new heights! 🌟
Text Version Tutorial: https://pylessons.com/transformers-nl...
OPUS datasets: https://opus.nlpl.eu/opus-100.php
#transformers #nlp #tokenizer #tensorflow #pytorch
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