Tokenizing Text
Tokenizing: Split input text into smaller units, such as individual tokens or words or sub-words. An important step in generating embeddings.
- Tokenization is a fundamental task when working on NLP tasks. It involves breaking down text into smaller units, known as tokens, which can be words, subwords, or characters.
- Efficient tokenization is crucial for the performance of language models, making it an essential step in various NLP tasks such as text generation, translation, and summarization.
Steps involved in Tokenizing Text
- Input text to Individual tokens or Tokenized Text
- Individual tokens to Token IDs (using Vocabulary or other techniques)
- Token IDs > Sliding window > Token Embeddings
- Token Embeddings + Positional Embeddings = Final Embeddings