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Model Card of StellarMilk/t5-base-newsqa-qag-trained

This model is fine-tuned version of t5-base for question & answer pair generation task on the StellarMilk/newsqa (dataset_name: default) via lmqg.

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="StellarMilk/t5-base-newsqa-qag-trained")

# model prediction
question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")
  • With transformers
from transformers import pipeline

pipe = pipeline("text2text-generation", "StellarMilk/t5-base-newsqa-qag-trained")
output = pipe("generate question and answer: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

Evaluation

Score Type Dataset

Training hyperparameters

The following hyperparameters were used during fine-tuning:

  • dataset_path: StellarMilk/newsqa
  • dataset_name: default
  • input_types: ['paragraph']
  • output_types: ['questions_answers']
  • prefix_types: ['qag']
  • model: t5-base
  • max_length: 512
  • max_length_output: 512
  • epoch: 14
  • batch: 2
  • lr: 0.0001
  • fp16: False
  • random_seed: 1
  • gradient_accumulation_steps: 2
  • label_smoothing: 0.0

The full configuration can be found at fine-tuning config file.

Citation

@inproceedings{ushio-etal-2022-generative,
    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
    author = "Ushio, Asahi  and
        Alva-Manchego, Fernando  and
        Camacho-Collados, Jose",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, U.A.E.",
    publisher = "Association for Computational Linguistics",
}
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Dataset used to train StellarMilk/t5-base-newsqa-qag-trained

Evaluation results

  • BLEU4 (Question & Answer Generation) on StellarMilk/newsqa
    self-reported
    3.180