So in the grand scheme of things I continue to be rather curious about things. I was just reading a blog post about How to Make LLMs Speak Your Language and I ran across an interesting term that I had read about and researched once before, but didn’t quite grok. So I asked one of my model friends to tell me more.
Needless to say it got a bunch of information right and its example wrong. I combined/edited all the right stuff and decided to save it here.
What is a one-hot encoded token vector?
A one-hot encoded token vector is a representation used in natural language processing (NLP) tasks, particularly in the context of text classification, language modeling, and other tasks involving categorical data.
Here’s how it works:
- Tokenization: First, the text is tokenized, meaning it’s split into individual tokens. These tokens could be words, characters, or subwords, depending on the chosen tokenization strategy.
- Vocabulary Creation: Next, a fixed vocabulary is created, which consists of all unique tokens found in the corpus of text. Each token is assigned a unique index in the vocabulary.
- One-Hot Encoding: For each token in the text, a one-hot encoded vector is created. This vector has the same length as the vocabulary, with a value of 1 at the index corresponding to the token’s position in the vocabulary and 0s elsewhere. In other words, each token is represented as a binary vector where only one element is “hot” (set to 1) corresponding to its index in the vocabulary, hence the name “one-hot encoding.”
For example, consider the following vocabulary:
Vocabulary: ['apple', 'banana', 'orange', 'grape']
And the text:
Text: "I like bananas and oranges."We would have the following one-hot encoded vectors:
'I': [0, 0, 0, 0] (Not in the vocabulary)
'like': [0, 0, 0, 0] (Not in the vocabulary)
'bananas': [0, 1, 0, 0]
'and': [0, 0, 0, 0] (Not in the vocabulary)
'oranges': [0, 0, 1, 0]So, the one-hot encoded vectors for ‘I’ and ‘like’ and ‘and’ would be all zeros since they are not present in the vocabulary, while the vectors for ‘bananas’ and ‘oranges’ would have a 1 in the corresponding position of the vocabulary.
This encoding preserves the categorical nature of the tokens and is often used as input for machine learning models in NLP tasks. However, it has limitations, such as the high dimensionality of the resulting vectors, especially for large vocabularies, and the inability to capture semantic similarities between tokens.
The cat is happy once more.
