fine-tuned-gpt2-medium-jokes

Fine-Tuned GPT-2 Medium: Programming Jokes

Model Summary

This model is a fine-tuned version of GPT-2 Medium, specifically trained to generate programming-related jokes. The fine-tuning was performed on a dataset of jokes . The model aims to produce humorous and contextually appropriate responses to prompts related to programming and technology.

Model Details

• Base Model: GPT-2 Medium (355 million parameters)
• Fine-tuning Dataset Programming Jokes Dataset (see https://huggingface.co/datasets/asfandyarazhar/programming-jokes-dataset)
• Language: English
• License: Apache 2.0
• Domain: Programming and Technology Humor

Training Procedure

The fine-tuning process involved training the GPT-2 Medium model on the programming jokes dataset using the Hugging Face transformers library. Key training configurations included:

• Epochs: 3
• Batch Size: 4
• Learning Rate: 5e-5
• Optimizer: AdamW

Model Performance

The fine-tuned model demonstrates an improved ability to generate relevant and humorous responses to prompts. While it captures the structure and style of programming jokes, the quality and coherence of the humor can vary. The model's humor is influenced by the data it was trained on, and users should consider this when interpreting outputs.

Intended Use

This model is designed for entertainment and educational purposes, providing a fun way to explore text generation in the context of programming humor. It can be used to generate jokes, study language patterns in humor, or as a starting point for further fine-tuning on similar datasets.

Limitations and Biases

• Humor Quality: The model's humor may not always align with human expectations of wit and may sometimes produce non-coherent or repetitive jokes.
• Cultural Bias: The model reflects the cultural and contextual biases present in the training data.
• Content Sensitivity: As with all generative models, there is a risk of generating content that may be offensive or inappropriate. Users should apply appropriate filters and reviews before deploying outputs.

How to Use

You can use this model with the transformers library in Python:

from transformers import pipeline

generator = pipeline('text-generation', model='your_username/fine-tuned-gpt2-medium-jokes')
result = generator("Why do programmers prefer dark mode?", max_length=50, num_return_sequences=1)
print(result[0]['generated_text'])