Understanding Transformers Part 1: How Transformers Understand Word Order
This article explores how transformers, a type of neural network, process and understand the order of words in a sentence. It explains the concept of word embeddings and positional encoding, which are key techniques used by transformers to capture the meaning and relationships between words.
Why it matters
Understanding how transformers process and understand the order of words is crucial for building effective natural language processing models using this powerful architecture.
Key Points
- 1Transformers cannot directly process text, so words need to be converted into numerical form using word embeddings
- 2Positional encoding is a technique used by transformers to keep track of the order of words in a sentence
- 3Unlike traditional models, transformers do not process words sequentially, so they need an additional way to understand word order
Details
The article starts by explaining that since transformers are a type of neural network, they operate on numerical data. To convert words into numbers, the most commonly used method is word embedding, which represents each word as a vector of numbers, capturing the meaning and relationships between words. The article then introduces the concept of positional encoding, which is a technique used by transformers to keep track of the order of words in a sentence. This is necessary because transformers do not process words sequentially, unlike traditional models. The article uses a simple example sentence,
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