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Transformers for beginners | What are they and how do they work
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Over the past five years, Transformers, a neural network architecture, have completely transformed state-of-the-art natural language processing.
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The encoder takes the input sentence and converts it into a series of numbers called vectors, which represent the meaning of the words. These vectors are then passed to the decoder, which generates the translated sentence.
Now, the magic of the transformer network lies in how it handles attention. Instead of looking at each word one by one, it considers the entire sentence at once. It calculates a similarity score between each word in the input sentence and every other word, giving higher scores to the words that are more important for translation.
To do this, the transformer network uses a mechanism called self-attention. Self-attention allows the model to weigh the importance of each word in the sentence based on its relevance to other words. By doing this, the model can focus more on the important parts of the sentence and less on the irrelevant ones.
In addition to self-attention, transformer networks also use something called positional encoding. Since the model treats words as individual entities, it doesn't have any inherent understanding of word order. Positional encoding helps the model to understand the sequence of words in a sentence by adding information about their position.
Once the encoder has calculated the attention scores and combined them with positional encoding, the resulting vectors are passed to the decoder. The decoder uses a similar attention mechanism to generate the translated sentence, one word at a time.
Transformers are the model behind GPT, BERT, and T5
#transformers #naturallanguageprocessing #nlp
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The encoder takes the input sentence and converts it into a series of numbers called vectors, which represent the meaning of the words. These vectors are then passed to the decoder, which generates the translated sentence.
Now, the magic of the transformer network lies in how it handles attention. Instead of looking at each word one by one, it considers the entire sentence at once. It calculates a similarity score between each word in the input sentence and every other word, giving higher scores to the words that are more important for translation.
To do this, the transformer network uses a mechanism called self-attention. Self-attention allows the model to weigh the importance of each word in the sentence based on its relevance to other words. By doing this, the model can focus more on the important parts of the sentence and less on the irrelevant ones.
In addition to self-attention, transformer networks also use something called positional encoding. Since the model treats words as individual entities, it doesn't have any inherent understanding of word order. Positional encoding helps the model to understand the sequence of words in a sentence by adding information about their position.
Once the encoder has calculated the attention scores and combined them with positional encoding, the resulting vectors are passed to the decoder. The decoder uses a similar attention mechanism to generate the translated sentence, one word at a time.
Transformers are the model behind GPT, BERT, and T5
#transformers #naturallanguageprocessing #nlp
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