Applied Text Generation using GPT and KerasNLP in Python

سرفصل های دوره

Dive into Hands-on TensorFlow and Python Programming with KerasNLP in Google Colab for an Immersive, Practical Learning

1 - Fundamentals

1 - Introduction

2 - About this Project

3 - Why Should we Learn

4 - Applications

5 - Why Python Keras and Google Colab

2 - Build and Train Model

6 - Setup the Working Directory

6 - text-generation.rar

7 - What is inside the traintxt and validtxt

8 - What is inside the codeipynb

9 - Open the Project

10 - Activate GPU

11 - Checks the availability of the GPU

12 - Mounts Google Drive

13 - Install Keras NLP

14 - Importing necessary libraries

15 - Define the paths to the training and validation text files

16 - Loads training and validation datasets and applies filtering

17 - Computes the vocabulary

18 - Initializes the WordPieceTokenizer

19 - Initializes the StartEndPacker layer

20 - Defines a preprocess function

21 - Preprocesses the training dataset

22 - Preprocesses the validation dataset

23 - Creates an embedding layer

24 - Building the TransformerDecoder layers

25 - Creating and compiling the model

26 - Summary of the models architecture

27 - Training the model

28 - Saving the trained model weights

29 - Generates a prompt token

30 - Generate the logits for the next token

31 - Creates a GreedySampler instance for text generation

32 - Creates a BeamSampler instance for text generation

33 - Creates a RandomSampler instance for text generation

34 - Creates a TopKSampler instance for text generation

35 - Creates a TopPSampler instance for text generation

36 - Define a custom callback

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تولید کننده: Udemy