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Model Card of lmqg/t5-large-subjqa-restaurants-qg

This model is fine-tuned version of lmqg/t5-large-squad for question generation task on the lmqg/qg_subjqa (dataset_name: restaurants) via lmqg.



from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="lmqg/t5-large-subjqa-restaurants-qg")

# model prediction
questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")
  • With transformers
from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/t5-large-subjqa-restaurants-qg")
output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")


Score Type Dataset
BERTScore 93.22 restaurants lmqg/qg_subjqa
Bleu_1 23.73 restaurants lmqg/qg_subjqa
Bleu_2 15.48 restaurants lmqg/qg_subjqa
Bleu_3 7.05 restaurants lmqg/qg_subjqa
Bleu_4 4.19 restaurants lmqg/qg_subjqa
METEOR 21.99 restaurants lmqg/qg_subjqa
MoverScore 63.25 restaurants lmqg/qg_subjqa
ROUGE_L 24.94 restaurants lmqg/qg_subjqa

Training hyperparameters

The following hyperparameters were used during fine-tuning:

  • dataset_path: lmqg/qg_subjqa
  • dataset_name: restaurants
  • input_types: ['paragraph_answer']
  • output_types: ['question']
  • prefix_types: ['qg']
  • model: lmqg/t5-large-squad
  • max_length: 512
  • max_length_output: 32
  • epoch: 6
  • batch: 16
  • lr: 0.0001
  • fp16: False
  • random_seed: 1
  • gradient_accumulation_steps: 4
  • label_smoothing: 0.15

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


    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 research-backup/t5-large-subjqa-restaurants-qg

Evaluation results