How to process transformer input?

CodeEmporium · Beginner ·🧠 Large Language Models ·3y ago

Key Takeaways

Processing transformer input involves encoding input strings into one-hot encoding vectors, converting them into 512-dimensional vectors, and adding position vectors using sine and cosine functions, before feeding them into the Transformer encoder layers. This process utilizes techniques such as padding, one-hot encoding, and positional encoding to prepare the input data for the Transformer model.

Full Transcript

how do we take these inputs and feed it to the Transformer encoder layers let's blow this up so we have the input string values and we will pad the values depending on how much we want to set the maximum input sequence length these could be words or sub words each of these words or sub words are then encoded into one hot encoding vectors and so we'll end up overall with a tensor of the maximum sequence length cross for every single one the max vocabulary size where one item in each case will be set to one these are sparse values and so we will encode them into 512 Dimension vectors each we can then compute position vectors using sine and cosine functions and add them to the original embedding vectors this will lead to 512 Dimension vectors for every single word and we can feed it now to the Transformer encoder for processing

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This video teaches how to process input data for Transformer models by encoding input strings into numerical vectors and adding positional information. The process involves several key steps, including one-hot encoding, vector conversion, and positional encoding, which are essential for preparing input data for Transformer models. By mastering these techniques, viewers can improve their understanding of Transformer models and their applications.

Key Takeaways
  1. Take input strings and pad them to the desired maximum input sequence length
  2. Encode each word or subword into a one-hot encoding vector
  3. Convert the one-hot encoding vectors into 512-dimensional vectors
  4. Compute position vectors using sine and cosine functions
  5. Add the position vectors to the original embedding vectors
  6. Feed the resulting vectors into the Transformer encoder layers
💡 The use of positional encoding allows the Transformer model to capture the order and position of the input words or subwords, which is essential for many natural language processing tasks.

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