T5 Large Generation Squad QuestionAnswer

🚀 t5-large fine-tuned to SQuAD for Generating Question+Answer

This model takes a context (e.g., a news article) as input and outputs a question <sep> answer. It generates extractive answers based on the SQuAD training data.

🚀 Quick Start

The t5-large model is fine-tuned on the SQuAD dataset. The input is the context/passage, and the output is the question followed by the answer. This is the first component (g1) in the question generation pipeline in our MQAG paper. You can also refer to the GitHub repo of this project: https://github.com/potsawee/mqag0.

The answers in the training data (SQuAD) are highly extractive; therefore, this model will generate extractive answers. If you would like to have abstractive questions/answers, you can use our model trained on the RACE dataset: https://huggingface.co/potsawee/t5-large-generation-race-QuestionAnswer.

✨ Features

• Input: context (e.g., news article).
• Output: question <sep> answer.
• Generates extractive answers based on SQuAD training data.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

Use the code below to get started with the model. You can also set do_sample=True in generate() to obtain different question-answer pairs.

>>> from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

>>> tokenizer = AutoTokenizer.from_pretrained("potsawee/t5-large-generation-squad-QuestionAnswer")
>>> model = AutoModelForSeq2SeqLM.from_pretrained("potsawee/t5-large-generation-squad-QuestionAnswer")

>>> context = r"""Chelsea's mini-revival continued with a third victory in a row as they consigned struggling Leicester City to a fifth consecutive defeat.
Buoyed by their Champions League win over Borussia Dortmund, Chelsea started brightly and Ben Chilwell volleyed in from a tight angle against his old club.
Chelsea's Joao Felix and Leicester's Kiernan Dewsbury-Hall hit the woodwork in the space of two minutes, then Felix had a goal ruled out by the video assistant referee for offside.
Patson Daka rifled home an excellent equaliser after Ricardo Pereira won the ball off the dawdling Felix outside the box.
But Kai Havertz pounced six minutes into first-half injury time with an excellent dinked finish from Enzo Fernandez's clever aerial ball.
Mykhailo Mudryk thought he had his first goal for the Blues after the break but his effort was disallowed for offside.
Mateo Kovacic sealed the win as he volleyed in from Mudryk's header.
The sliding Foxes, who ended with 10 men following Wout Faes' late dismissal for a second booking, now just sit one point outside the relegation zone.
""".replace('\n', ' ')

>>> inputs = tokenizer(context, return_tensors="pt")
>>> outputs = model.generate(**inputs, max_length=100)
>>> question_answer = tokenizer.decode(outputs[0], skip_special_tokens=False)
>>> question_answer = question_answer.replace(tokenizer.pad_token, "").replace(tokenizer.eos_token, "")
>>> question, answer = question_answer.split(tokenizer.sep_token)

>>> print("question:", question)
question:  Who scored the winner for Chelsea?
>>> print("answer:", answer)
answer:  Mateo Kovacic

Advanced Usage

# You can set do_sample=True in generate() to obtain different question-answer pairs.
# For example:
outputs = model.generate(**inputs, max_length=100, do_sample=True)

📚 Documentation

Generating Distractors (other options in a multiple-choice setup)

Context ---> Question + (A) Answer (B) Distractor1 (C) Distractor2 (D) Distractor3 Please refer to our distractor generation model, e.g., https://huggingface.co/potsawee/t5-large-generation-race-Distractor.

📄 License

This model is licensed under the Apache-2.0 license.

🔧 Technical Details

The t5-large model is fine-tuned to the SQuAD dataset, which is used for question generation. The input is the context/passage, and the output is the question followed by the answer. This is the first component in the question generation pipeline (i.e., g1) in our MQAG paper.

📄 Citation

@article{manakul2023mqag,
  title={MQAG: Multiple-choice Question Answering and Generation for Assessing Information Consistency in Summarization},
  author={Manakul, Potsawee and Liusie, Adian and Gales, Mark JF},
  journal={arXiv preprint arXiv:2301.12307},
  year={2023}
}