dexxiez/quora-title-gen

Quora Title Generator

A fine-tuned GPT-2 model specialized for generating Quora-style question titles. This model has been trained on a curated dataset of Quora question titles to learn the patterns and style of effective question formulation.

Model Description

This model is a fine-tuned version of GPT-2 specifically designed to generate compelling and realistic Quora question titles. It can be used for:

• Question Title Generation: Generate realistic Quora-style questions
• Text Completion: Complete partial questions or topics into full titles
• Content Ideation: Generate ideas for question-based content

Key Features

• 🎯 Specialized Training: Fine-tuned on high-quality Quora question titles
• 🎛️ Configurable Generation: Adjustable temperature, top-p, and top-k parameters
• 💡 Creative Output: Generates diverse and contextually appropriate questions
• 📊 Quality Dataset: Trained on the dexxiez/quora-titles dataset

Usage

Quick Start

from transformers import GPT2LMHeadModel, GPT2Tokenizer

model_name = "dexxiez/quora-title-gen"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

# Generate a random title
input_text = "<|startoftext|>"
inputs = tokenizer.encode(input_text, return_tensors="pt")
outputs = model.generate(
    inputs, 
    max_length=50, 
    temperature=0.8, 
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)
title = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(title)

Text Completion

# Complete a partial question
prompt = "<|startoftext|>How to learn"
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(
    inputs,
    max_length=100,
    temperature=0.8,
    top_p=0.95,
    top_k=50,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)
completed_title = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(completed_title)

Training Details

Training Data

• Dataset: dexxiez/quora-titles
• Content: Curated collection of high-quality Quora question titles
• Format: Plain text titles with special token formatting (<|startoftext|>title<|endoftext|>)
• Quality: Filtered for relevance and engagement

Training Procedure

• Base Model: GPT-2 (124M parameters)
• Training Epochs: 3
• Train/Eval Split: 90/10
• Max Sequence Length: 128 tokens
• Special Tokens: <|startoftext|>, <|endoftext|>, <|pad|>

Generation Parameters

Example Outputs

Random Generation:

• "What are the most effective strategies for learning a new programming language?"
• "How can introverts succeed in networking events?"
• "Why do some people find it easier to learn languages than others?"

Text Completion:

Input: "How to learn"
Output: "How to learn data science without a computer science background?"

Input: "What is the best way"
Output: "What is the best way to prepare for coding interviews at FAANG companies?"

Dataset Information

This model was trained on the dexxiez/quora-titles dataset, which contains:

• High-quality Quora question titles
• Diverse topic coverage
• Natural language patterns typical of question formulation
• Preprocessed and filtered for training optimization

Limitations

• Optimized specifically for English Quora-style questions
• May occasionally generate incomplete or repetitive text
• Performance varies with generation parameters
• Best results with topics similar to training data distribution

Ethical Considerations

• Generated content should be reviewed before publication
• May reflect biases present in the original Quora dataset
• Not intended for generating harmful or inappropriate content
• Use responsibly for content creation and ideation

Citation

@misc{quora-title-gen,
  title={Quora Title Generator: Fine-tuned GPT-2 for Question Generation},
  author={dexxiez},
  year={2025},
  url={https://huggingface.co/dexxiez/quora-title-gen}
}

License

MIT License