Text generation algorithms are taking over the world unleashing new tech revolutions around the globe🌏🌍🌎

Here are 4 techniques that you must know which is used in text-generation models

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1. Markov Chain:

A statistical model that predicts the next word based on the probability distribution of the previous words in the text. (Cont...)

It generates new text by starting with an initial state, and then repeatedly sampling the next word based on the probability distribution learned from the input text.

2. Sequence-to-Sequence (Seq2Seq) Model:

A deep learning model that consists of two recurrent neural networks (RNNs), an encoder, and a decoder. (Cont...)

The encoder takes the input text and produces a fixed-length vector representation, while the decoder generates the output text based on the vector representation.

3. Generative Adversarial Network (GAN):

A deep learning model that consists of two neural networks, a generator, and a discriminator. The generator produces new text samples, while the discriminator tries distinguishing between the generated text and the simple text. (Cont...)

The two networks are trained in an adversarial manner, where the generator tries to produce more realistic text, while the discriminator tries to become better at recognizing fake text.

4. Transformer-based Models:

Transformer models are neural network architectures designed for NLP tasks, such as text classification and machine translation. They have been shown to perform well on text-generation tasks as well.

Learn more about NLP and LLMs from the awesome course at @DataKwery

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www.datakwery.com/post/fundamentals-of-nlp-guide/?utm_source=avi&utm_medium=blog

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