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nithin521/
Meme_Caption_Generator

Name: Meme_Caption_Generator
Rating: 2.5 (44 reviews)

safetensors

gpt2

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😂 Meme Caption Generator

A fine-tuned GPT-2 language model designed to generate short, humorous, and social-media-style meme captions from user-provided prompts.

The model learns common meme caption patterns such as:

When ...
Me when ...
POV: ...
How it feels when ...
That one friend who ...

It can generate multiple caption variations for a single prompt.

🚀 Model Overview

Property	Details
Base Model	GPT-2
Task	Meme Caption Generation
Language	English
Framework	PyTorch
Library	🤗 Transformers
Model Type	Autoregressive Language Model
Output	Short Meme Captions
Fine-Tuning	Supervised Fine-Tuning
Format	Safetensors

✨ Features

Generate meme captions from a text prompt
Generate multiple caption variations
Supports prompt-based and random caption generation
Adjustable text-generation parameters
Designed for short, humorous, social-media-style text

Example:

Prompt

When you finally get your salary

Possible generations

text

When you finally get your salary and your bills were waiting for you.

Me checking my bank account after getting paid.

POV: You finally get paid but somehow you're still broke.

🧠 Model Architecture

This model is based on GPT-2, an autoregressive Transformer language model.

During fine-tuning, the model was trained to learn the distribution and writing style of meme captions.

Each training example follows a structure similar to:

<|capbos|> meme caption <|capeos|>

where:

<|capbos|> marks the beginning of a caption
<|capeos|> marks the end of a caption
<|cappad|> is used as the padding token

This allows the model to learn where meme captions begin and end.

📊 Training

The model was fine-tuned on a custom meme-caption dataset containing short English meme captions collected and processed for this project.

The dataset was cleaned and prepared before fine-tuning to improve training quality.

The preprocessing pipeline included:

Caption extraction
Text cleaning
Duplicate removal
Dataset preparation
Tokenization
Train/validation split
GPT-2 fine-tuning

The training process used:

PyTorch
Hugging Face Transformers
AdamW optimization
Learning-rate scheduling
Gradient accumulation
Validation monitoring
Early stopping

⚙️ Generation Parameters

The model can be used with sampling-based generation.

Recommended starting configuration:

python

max_new_tokens = 25
temperature = 0.75
top_k = 40
top_p = 0.90
repetition_penalty = 1.1
no_repeat_ngram_size = 3
do_sample = True

For more creative outputs, temperature and sampling parameters can be increased.

For more predictable outputs, temperature can be decreased.

💻 Usage

Using Transformers

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "nithin521/Meme_Caption_Generator"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

model.eval()

# Get special tokens directly from tokenizer
BOS_TOKEN = tokenizer.bos_token
EOS_TOKEN = tokenizer.eos_token

tokenizer.pad_token = tokenizer.eos_token

prompt = "When you finally get your salary"

# Use tokenizer's BOS token
text = BOS_TOKEN + " " + prompt

inputs = tokenizer(
    text,
    return_tensors="pt"
)

outputs = model.generate(
    **inputs,
    max_new_tokens=25,
    temperature=0.75,
    top_k=40,
    top_p=0.90,
    do_sample=True,
    repetition_penalty=1.1,
    no_repeat_ngram_size=3,
    num_return_sequences=5,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.pad_token_id
)

for output in outputs:

    # Remove the input prompt
    generated_tokens = output[
        inputs["input_ids"].shape[1]:
    ]

    caption = tokenizer.decode(
        generated_tokens,
        skip_special_tokens=False
    )

    # Stop at tokenizer's EOS token
    if EOS_TOKEN:
        caption = caption.split(EOS_TOKEN)[0]

    # Remove tokenizer special tokens
    caption = caption.replace(BOS_TOKEN or "", "")
    caption = caption.replace(tokenizer.pad_token or "", "")

    caption = caption.strip()

    if caption:
        print(caption)

🎛️ Generation Controls

The model works particularly well when generation parameters are adjusted according to the desired output.

Parameter	Effect
`temperature`	Controls randomness
`top_k`	Limits token selection to the top K candidates
`top_p`	Controls nucleus sampling
`max_new_tokens`	Controls maximum caption length
`repetition_penalty`	Reduces repetitive text
`no_repeat_ngram_size`	Prevents repeated phrases

Recommended values:

text

Temperature:       0.75
Top-K:             40
Top-P:             0.90
Max New Tokens:    25

🌐 Interactive Demo

The model is deployed as an interactive Gradio application on Hugging Face Spaces.

Demo:

https://huggingface.co/spaces/nithin521/Meme-caption-generation

The application allows users to:

Enter a meme prompt
Generate multiple captions
Adjust generation parameters
Experiment with different levels of randomness

🎯 Intended Use

This model is intended for:

Meme caption generation
Social-media content experimentation
NLP demonstrations
Generative AI projects
Educational purposes
Exploring fine-tuned language models

⚠️ Limitations

The model may occasionally generate:

Grammatically incorrect sentences
Repetitive captions
Incomplete captions
Semantically unrelated text
Offensive or inappropriate language
Memorized patterns from the training dataset
Captions that require human editing

Generated captions should therefore be reviewed before being used publicly.

The model is designed for creative generation rather than factual or reliable text generation.

🔒 Safety & Responsible Use

The model generates text based on patterns learned from its training data and does not have an independent understanding of the generated content.

Users should review generated content before publishing it.

The model should not be used to generate targeted harassment, hateful content, misinformation, or other harmful material.

📚 Technologies Used

Python
PyTorch
Hugging Face Transformers
Hugging Face Hub
GPT-2
Gradio
Safetensors

🏗️ Project Pipeline

The complete project includes a data-processing and caption-generation pipeline:

text

Meme Images
     ↓
Text Detection
     ↓
YOLO-based Caption Region Detection
     ↓
Caption Cropping
     ↓
OCR / Text Extraction
     ↓
Dataset Cleaning
     ↓
Duplicate Removal
     ↓
GPT-2 Fine-tuning
     ↓
Meme Caption Generation
     ↓
Gradio Web Application

The YOLO-based text detector was developed to identify meme text regions and improve the quality of captions extracted from meme images during dataset preparation.

📈 Future Improvements

Potential improvements include:

Increasing the size and diversity of the training dataset
Improving dataset quality and caption filtering
Adding automated generation-quality ranking
Removing noisy or incomplete generations
Fine-tuning larger language models
Improving semantic control over generated captions
Adding multilingual meme caption generation
Improving safety filtering
Adding image-to-caption generation

👨‍💻 Project

This model was developed as part of an end-to-end AI Meme Caption Generator project combining computer vision, natural language processing, and generative AI.

Author

Nithin Kumar

Hugging Face: https://huggingface.co/nithin521