What are the best practices for data preprocessing in NLP models?
An example of an AI chatbot conversation about data preprocessing techniques for NLP models.
What are the best practices for data preprocessing in NLP models?
Data preprocessing is crucial for NLP models. Key practices include tokenization, removing stop words, and stemming.
Can you explain tokenization and why it's important?
Tokenization is the process of breaking text into individual words or phrases. It's important because it helps the model understand the structure of the text.
What tools can I use for data preprocessing?
Popular tools include NLTK, SpaCy, and Hugging Face's Transformers library, which offer various preprocessing functionalities.
How do I handle missing data in my dataset?
You can handle missing data by either removing those entries, filling them with a default value, or using techniques like interpolation.
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