T5 Base Finetuned Question Generation Ap

🚀 T5-base fine-tuned on SQuAD for Question Generation

This project fine-tunes Google's T5 on SQuAD v1.1 for Question Generation. It simply prepends the answer to the context.

📚 Details of T5

The T5 model was introduced in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu. Here is the abstract:

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts every language problem into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our dataset, pre-trained models, and code.

📦 Details of the downstream task (Q&A) - Dataset

Dataset ID: squad from Huggingface/NLP

How to load it from nlp

train_dataset  = nlp.load_dataset('squad', split=nlp.Split.TRAIN)
valid_dataset = nlp.load_dataset('squad', split=nlp.Split.VALIDATION)

Check out more about this dataset and others in NLP Viewer

🔧 Model fine-tuning

The training script is a slightly modified version of this awesome one by Suraj Patil. He also conducted great research on Question Generation.

💻 Usage Examples

Basic Usage

# Tip: By now, install transformers from source

from transformers import AutoModelWithLMHead, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")
model = AutoModelWithLMHead.from_pretrained("mrm8488/t5-base-finetuned-question-generation-ap")

def get_question(answer, context, max_length=64):
  input_text = "answer: %s  context: %s </s>" % (answer, context)
  features = tokenizer([input_text], return_tensors='pt')

  output = model.generate(input_ids=features['input_ids'], 
               attention_mask=features['attention_mask'],
               max_length=max_length)

  return tokenizer.decode(output[0])

context = "Manuel has created RuPERTa-base with the support of HF-Transformers and Google"
answer = "Manuel"

get_question(answer, context)

# output: question: Who created the RuPERTa-base?

📄 License

This project is under the apache-2.0 license.

📖 Citation

If you want to cite this model you can use this:

@misc{mromero2021t5-base-finetuned-question-generation-ap,
  title={T5 (base) fine-tuned on SQUAD for QG via AP},
  author={Romero, Manuel},
  publisher={Hugging Face},
  journal={Hugging Face Hub},
  howpublished={\url{https://huggingface.co/mrm8488/t5-base-finetuned-question-generation-ap}},
  year={2021}
}

Created by Manuel Romero/@mrm8488 | LinkedIn

Made with ♥ in Spain