gpt2_question_answering_squad2

Introduction

The gpt2_question_answering_squad2 model is a specialized version of GPT-2 fine-tuned for question answering tasks using the SQuAD2 dataset. It is designed to generate text-based answers to questions based on provided context.

Architecture

The model is based on the GPT-2 architecture, a transformer-based language model known for its strength in text generation tasks. It utilizes the GPT2LMHeadModel for text generation and GPT2Tokenizer for encoding inputs.

Training

The model was fine-tuned over three years ago on the SQuAD2 dataset, a popular benchmark for question answering systems. This dataset includes a variety of question and answer pairs, enhancing the model's ability to understand and generate relevant answers.

Guide: Running Locally

To run the gpt2_question_answering_squad2 model locally:

1. Install Hugging Face Transformers:

   pip install transformers
   

2. Load the Model and Tokenizer:

   from transformers import GPT2Tokenizer, GPT2LMHeadModel
   
   tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
   model = GPT2LMHeadModel.from_pretrained("danyaljj/gpt2_question_answering_squad2")
   

3. Generate Answers:

   input_ids = tokenizer.encode("There are two apples on the counter. Q: How many apples? A:", return_tensors="pt")
   outputs = model.generate(input_ids)
   print("Generated:", tokenizer.decode(outputs[0], skip_special_tokens=True))
   

4. Output: This script will generate: Generated: There are two apples on the counter. Q: How many apples? A: two

For optimal performance, consider using cloud GPUs such as those available on AWS, Google Cloud, or Azure.

License

The model follows the licensing terms provided by Hugging Face and the original authors. Ensure to review and comply with these terms when using the model.