AI Tutorial 144: Natural Language Processing (NLP) with Transformers235

## Introduction
Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) that deals with the interaction between computers and human (natural) languages. NLP enables computers to understand, interpret, and generate human language, making it possible for them to engage in meaningful conversations, answer questions, translate languages, and perform other language-related tasks.
In recent years, transformer models have emerged as a powerful technique for NLP. Transformers are neural network architectures that have achieved state-of-the-art results on a wide range of NLP tasks. They are particularly well-suited for tasks that involve understanding the relationships between words and sentences in a text.
## What are Transformers?
Transformers were introduced in the paper "Attention Is All You Need" by Vaswani et al. (2017). They are based on the concept of attention, which allows the model to focus on specific parts of the input sequence when making predictions. This is in contrast to recurrent neural networks (RNNs), which process the input sequence one element at a time.
Transformers consist of two main components:
* Encoder: The encoder converts the input sequence into a fixed-length vector. This vector represents the meaning of the input sequence and is used by the decoder to generate the output.
* Decoder: The decoder generates the output sequence one element at a time. It uses the vector from the encoder to generate the first element of the output sequence. It then uses the vector from the encoder and the previously generated elements of the output sequence to generate the next element.
## How Transformers Work
Transformers work by attending to different parts of the input sequence. This allows them to capture the relationships between words and sentences in the text. The attention mechanism is implemented using a set of scaled dot-product attention layers.
Each attention layer takes three inputs:
* Query: A vector that represents the current element of the output sequence.
* Key: A vector that represents each element of the input sequence.
* Value: A vector that represents each element of the input sequence.
The attention layer computes a dot product between the query and each of the keys. The results are scaled and then used to compute a weighted average of the values. The output of the attention layer is a vector that represents the context of the current element of the output sequence.
The output of the attention layer is then passed through a feed-forward neural network. The output of the feed-forward neural network is the final output of the attention layer.
## Applications of Transformers
Transformers have a wide range of applications in NLP, including:
* Machine translation: Transformers can translate text from one language to another.
* Question answering: Transformers can answer questions about a given text.
* Text summarization: Transformers can summarize a given text.
* Named entity recognition: Transformers can identify named entities (e.g., people, places, organizations) in a given text.
* Part-of-speech tagging: Transformers can identify the part of speech of each word in a given text.
## Conclusion
Transformers are a powerful technique for NLP. They have achieved state-of-the-art results on a wide range of NLP tasks. Transformers are based on the concept of attention, which allows them to capture the relationships between words and sentences in a text. This makes them particularly well-suited for tasks that involve understanding the meaning of text.

2025-02-18

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