Joke-Generation-and-Rating-Using-LLM

Within this repository, you will find code for a humor-oriented bot. This bot has the ability to produce various categories of jokes and can also assess the quality of jokes.

Objective

Following are the objective of this project:

• Finetune a large language model for joke generation.
• Finetune a large language model to assess the quality of a joke.
• Create an interactive humor-oriented bot which is capable of generating jokes from different genre.

This project was done as part of the AI Comedy Club Challenge.

Steps to replicate the project

Install Dependencies

pip install -r requirements.text

All the configuration need for data loading, data cleaning, model training, model inference and so on are stored in the below file.

 src/config.py 

Running the Bot

Run the below command to start interacting with the bot.

python src/joke_bot.py

The joke bot can be run directly without the need for training the joke-generator and joke-rater model. I have uploaded my trained models to HuggingFace. While inference, the joke-bot will use these models. Links to the models are provided below:

Joke Generator Model

Joke Rater Model

Incase if you want to fine-tune the models again, please follow the below steps.

Train the joke generator and joke rater modesls

As mentioned before, all the hyperparameters necessary for model finetuning is stored in the src/config.py file. Adapt the necessary hyperparmeters by editing this file before fine-tuning the models. After adjusting the hyperparametes, run the below command to fine-tune the models.

python src/train_models.py

Dataset

In this section we will discuss the dataset used for model training and the preprocessing steps done.

rjokes Dataset

rJokes Dataset [1]: a collection of over 550,000 jokes posted over an 11-year period on the Reddit r/Jokes subreddit channel. This dataset also provides quantitative metrics for the level of humor (0 - 11) in each joke, determined by subreddit user feedback (upvoted and downvoted).

After the exploration of the dataset, for this challenge, I am only planning to use jokes with labels from 1 to 10.

1

GitHub link to download the dataset: https://github.com/orionw/rJokesData/tree/master/data

Data Preprocessing

The dataset underwent the following preprocessing steps:

• Removed special characters
• Removed emojis
• Removed jokes which have less than 5 words
• Removed toxic jokes using Google's perspective API

Models Used

I have fine-tuned two models for this project, one for joke generation and another for rating the jokes.

Joke Generation

For the task of joke generation, I have used the GPT-2 model. After preprocessing the rjokes dataset contains jokes of 10 different humor levels, 1 being the least funny joke and 10 being the most funny joke. I added a prompt to the begining of each joke, the added prompt depends on the humor level of the joke. This was done so that, I can make the joke bot generate diverse jokes by randomly selecting a prompt from the available 10 prompt. Below I have provided the prompts that were used.

  self.prompts = {
            10: "Give me a hilarious joke that deserves a perfect 10: ",
            9: "Let's challenge the comedy genius within you. Craft a joke that is an absolute 9 in humor: ",
            8: "Make me smile with a joke worth a solid 8: ",
            7: "Time to test your wit. Create a rib-tickler with a humor rating of 7: ",
            6: "I need a side-splitting joke that's at least an 6 on the humor scale: ",
            5: "Tickle my funny bone with a joke that falls between 5 and 6 on the humor scale:",
            4: "Generate a joke with a humor score of 4:",
            3: "Time for some light-hearted humor, a casual joke ranking around 3 should do:",
            2: "Even the best comedians start somewhere. Share a joke with a humor level around 2:",
            1: "Generate a joke which not that funny:"
        }

The hyperparameters used for joke generator model fine-tuning are maintained in src/config.py file. Below are the hyperparameters used.

  class JokeGeneratorModelConfig:
    task = "generation"
    model_name = "gpt2"
    output_dir = 'models/joke_gen_model/output'
    num_train_epochs = 4
    per_device_train_batch_size = 4
    per_device_eval_batch_size = 4
    save_steps = 1000
    save_total_limit = 2
    evaluation_strategy = 'epoch'
    logging_steps = 500
    learning_rate = 2e-5
    logging_dir = 'models/joke_gen_model/logs'
    report_to = "none"

Rating the Jokes

For the task of joke rating I have used the BERT-BASE model. The preprocessed rjokes dataset was used for fine-tuning. A classification head was added to the BERT model and was fine-tuned to predict the humor level of the joke. The hyperparameters used for joke rater model fine-tuning are maintained in src/config.py file. Below are the hyperparameters used.

  class JokeRaterModelConfig:
    task = "classification"
    model_name = "bert-base-uncased"
    num_labels = 10
    output_dir = 'models/joke_rater_model/output'
    num_train_epochs = 5
    per_device_train_batch_size = 8
    per_device_eval_batch_size = 8
    save_steps = 1000
    save_total_limit = 2
    evaluation_strategy = 'epoch'
    logging_steps = 500
    learning_rate = 2e-5
    logging_dir = 'models/joke_rater_model/logs'
    report_to = "none"

User Interaction

Apart from the jokes generated using the GPT-2 model, joke-bot can also generate two other types of jokes: Christmas and Programming jokes. These are jokes created using the Jokes API. Everytime a user requests a joke of this type, we send a get request to the Jokes API and recieve a joke which is shown to the user. The joke bot also considers the user's mood and joke preference while deciding which joke should be shown to the user.

Additionally, once the joke is shown to the user, joke bot asks the user to rate the joke (from 1 - 10). This data can be later used for improving the model preformance as well. Also the rating for the joke from the joke rater model is also shown to the user.