中文

Model Card of lmqg/t5-small-squad-qg

This model is fine-tuned version of t5-small for question generation task on the lmqg/qg_squad (dataset_name: default) via lmqg .

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="lmqg/t5-small-squad-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-small-squad-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.")

Evaluation

Score Type Dataset
BERTScore 90.2 default lmqg/qg_squad
Bleu_1 56.86 default lmqg/qg_squad
Bleu_2 40.59 default lmqg/qg_squad
Bleu_3 31.05 default lmqg/qg_squad
Bleu_4 24.4 default lmqg/qg_squad
METEOR 25.84 default lmqg/qg_squad
MoverScore 63.89 default lmqg/qg_squad
ROUGE_L 51.43 default lmqg/qg_squad
  • Metric (Question & Answer Generation, Reference Answer) : Each question is generated from the gold answer . raw metric file
Score Type Dataset
QAAlignedF1Score (BERTScore) 95.14 default lmqg/qg_squad
QAAlignedF1Score (MoverScore) 69.79 default lmqg/qg_squad
QAAlignedPrecision (BERTScore) 95.19 default lmqg/qg_squad
QAAlignedPrecision (MoverScore) 70.09 default lmqg/qg_squad
QAAlignedRecall (BERTScore) 95.09 default lmqg/qg_squad
QAAlignedRecall (MoverScore) 69.51 default lmqg/qg_squad
Score Type Dataset
QAAlignedF1Score (BERTScore) 92.26 default lmqg/qg_squad
QAAlignedF1Score (MoverScore) 63.83 default lmqg/qg_squad
QAAlignedPrecision (BERTScore) 92.07 default lmqg/qg_squad
QAAlignedPrecision (MoverScore) 63.92 default lmqg/qg_squad
QAAlignedRecall (BERTScore) 92.48 default lmqg/qg_squad
QAAlignedRecall (MoverScore) 63.82 default lmqg/qg_squad
  • Metrics (Question Generation, Out-of-Domain)
Dataset Type BERTScore Bleu_4 METEOR MoverScore ROUGE_L Link
lmqg/qg_squadshifts amazon 89.94 5.45 20.75 59.79 22.97 link
lmqg/qg_squadshifts new_wiki 92.61 10.48 26.21 65.05 28.11 link
lmqg/qg_squadshifts nyt 91.71 6.97 23.66 62.86 23.03 link
lmqg/qg_squadshifts reddit 89.57 4.75 19.8 59.23 20.1 link
lmqg/qg_subjqa books 87.4 0.0 12.3 55.34 10.88 link
lmqg/qg_subjqa electronics 87.12 1.16 15.49 55.55 15.62 link
lmqg/qg_subjqa grocery 87.22 0.52 14.95 57.12 12.63 link
lmqg/qg_subjqa movies 86.84 0.0 12.11 55.01 12.63 link
lmqg/qg_subjqa restaurants 87.49 0.0 12.67 55.04 11.53 link
lmqg/qg_subjqa tripadvisor 88.4 1.46 15.53 55.91 14.24 link

Training hyperparameters

The following hyperparameters were used during fine-tuning:

  • dataset_path: lmqg/qg_squad
  • dataset_name: default
  • input_types: ['paragraph_answer']
  • output_types: ['question']
  • prefix_types: ['qg']
  • model: t5-small
  • max_length: 512
  • max_length_output: 32
  • epoch: 9
  • batch: 64
  • lr: 0.0001
  • fp16: False
  • random_seed: 1
  • gradient_accumulation_steps: 1
  • label_smoothing: 0.15

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",
}