How to Train an AI-Powered Story Generator Model with Python
A story generator is a tool that uses advanced Artificial Intelligence algorithms to generate unique and compelling stories according to the given prompt.
Don’t be surprised due to the capability of such tools, this is because you can also train and create an AI-based story generator with good programming skill in Python. In this blog post, we are going to discuss a step-by-step procedure in detail.
How to Train an AI-Powered Story Generator Model with Python
Procedure for Training an AI Story Generator Using Python
Below are the most essential steps that you need to follow in order to train an AI-based story generator using Python.
1.Download & Install Python Libraries
The first step for you is to download and install a program that helps you effectively train the AI-based story generator.
Pandas: It is an easy-to-use Python tool and library that is widely used for data manipulation and analysis.
NLTK: Natural Language Toolkit is used for tasks like tokenization and stop-word removal.
Tensorflow: It is a Python library that is specifically used for the development and training of different models.
Keras: It is used for creating and training models.
You can easily download these libraries by doing a simple Google search. However, before downloading make sure your system fulfills the required system criteria.
2. Import Libraries
Once the installation is done, you can then proceed toward the importing process of all the aforementioned libraries into the code editor you are using such as Visual Studio code, etc.
For your ease, below we have mentioned the Python code that you can use for importation.
3. Perform Data Acquisition
From here, the training process of an AI-powered story generator will actually start. Perform data acquisition first which will involve gathering diverse datasets on which the story generator will be trained. For this, you can refer to online blogs, novels, and journals.
However, make sure the data you are collecting perfectly represents the writing style of your story-generator targeted audience.
Once you have gathered all the data, it is time to “Save it” as a CSV file. Now, load the file into the code editor using the prompt below.
The “Stories.CSV” is the name against which we saved the required file into our computer. In your case, you have to provide your file name here.
4. Preprocess Data
The processing is basically a process in which Python will tokenize the input file’s content into individual words and phrases. Besides this, it will also turn all the uppercase letters to lowercase.
Finally, the process will also remove punctuation and stop words from the uploaded text. sometimes you need to follow the NLP text processing Techniques.
Below we mentioned the code that can be used for preprocessing.
5. Pick & Define a Language Model Architecture
This is obvious, you will have a pick language model on which your story generator will work. There are multiple language models available that you can consider going with. Some of them are:
Recurrent Neural Networks (RNNs): This language model is highly suitable for sequential data like blogs, stories, and other forms of text.
Transformers: These models have gained too much popularity in recent years, all because of their ability to scan and understand long-range dependencies.
Once you have picked the language model, then it is necessary to define it within your Python code. For this blog, we choose RNNs. Remember, the defining process may vary depending on the language model.
In RNNs, there are four major defining elements. The first is – the input layer that accepts the tokenized text as input. Then comes the embedding layer that embeds both words and sub-words into a continuous vector space.
The third layer is the hidden layer, and the number and type of hidden layer completely depends on the architecture model. Finally, the output layer contributes to predicting the text in a tokenized sequence.
For a better understanding of you, below is the Python code for picking and defining language model.
6. Training the Model
This is the step that requires the most effort and attention. The training process will require taking care of multiple things that we have listed below.
Lost Function: You should use a lost function such as cross-empty loss to effectively measure the difference between the predicted and actual next tokens.
Pick Optimizer: To train a story generator model, you also have to pick an optimizer like Adam, SGD, etc. Such optimizers will help upgrade the model’s weight during training.
Hyperparameter Tuning: To find the optimal configuration, you should experiment with different hyperparameters like batch size, no. of epochs, etc.
It is important to note that the main character in the training process will be the “Keras” library that we downloaded, installed, and imported in the first two steps. The library will tokenize the preprocessed text to automatically train the model.
Here is the code that can be used for training!
7.Generating Stories
Finally, the last step! When the training process is done. It is time to test the model by generating stories using the procedure below:
Input or Seed Text: Provide starting text or prompt to the model, such as “Once upon a time.” Do not forget to provide the desired length.
Feed the text Again: You should also feed the generated text back into the model so that the next token is predicted.
In the Python code below, we provided a “Once upon a time” prompt, and a limit of 100 words.
Now, just use the “Print” command to get a unique and compelling story from the model according to the given details.
Example of an AI Story Generator That is Trained Using Python
Although, there are numerous tools available online that are trained on Python algorithms as we did above. One good example is the AI story generator by Summarizer.org. It trained diverse datasets using Python to generate engaging stories of different types (Original, Classis, Horror, etc.) and lengths (Short, Medium, and Long).
To demonstrate better, we provided the generator with the following prompt to see what results it would provide.
Input prompt: Three men lost in the desert and struggling for their survival
As the screenshot tells us, the generator has crafted a unique story according to the prompt within seconds.
Final Words
Training an AI-powered story-generator using Python is no doubt a skill task. However, with decent programming skills and by following the right approach, it can be done without any frustration and disappointment. In this blog post, we have discussed the procedure in detail, along with code examples.
