Training Models: Techniques and Considerations

 

Preparing Data for Language Models

Preparing data for language models involves data cleaning, data preparation, data curation, and data augmentation.

Data Cleaning and Preparation

Data cleaning involves removing irrelevant data, correcting errors, and normalizing the text to enable consistent processing. 

This includes techniques such as removing HTML tags and punctuation and correcting spelling mistakes. 

Data preparation involves tokenizing the text, breaking it down into smaller units such as words or characters, and encoding it into a form that the model can process.

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