README.md

metadata

license: cc-by-4.0
metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
language: en
datasets:
  - lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
  - question generation
widget:
  - text: >-
      <hl> Beyonce <hl> further expanded her acting career, starring as blues
      singer Etta James in the 2008 musical biopic, Cadillac Records.
    example_title: Question Generation Example 1
  - text: >-
      Beyonce further expanded her acting career, starring as blues singer <hl>
      Etta James <hl> in the 2008 musical biopic, Cadillac Records.
    example_title: Question Generation Example 2
  - text: >-
      Beyonce further expanded her acting career, starring as blues singer Etta
      James in the 2008 musical biopic,  <hl> Cadillac Records <hl> .
    example_title: Question Generation Example 3
model-index:
  - name: lmqg/mt5-small-squad-qg
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_squad
          type: default
          args: default
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 21.65
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 48.95
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 23.83
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 90.01
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 62.75
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_dequad
          type: default
          args: default
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 9.242783121165897e-12
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.01556150764938016
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.04809700451843158
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.7353078946893743
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.5036973829954939
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_esquad
          type: default
          args: default
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 0.0059191752064594125
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.05208940592236566
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.06021086135293597
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.7494422899749911
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.5062373132800192
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_frquad
          type: default
          args: default
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 0.0171464639522496
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.1583673053928925
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.08244973027319356
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.7291012183458674
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.509610854598101
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_itquad
          type: default
          args: default
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 0.005438910607183992
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.05010570221421983
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.05890828426558759
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.7260160158030385
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.5023119088393686
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_jaquad
          type: default
          args: default
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 4.4114578660129224e-8
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.06084267343290677
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.005149267426183168
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.6608093198082075
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.46526108687696893
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_koquad
          type: default
          args: default
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 1.4750917137316939e-12
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.0006466767450454226
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.007310046912436679
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.6634288882769679
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.4586124640357038
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_ruquad
          type: default
          args: default
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 4.229109829516021e-12
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.009881091250723615
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.017796529053904556
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 0.7089446693028568
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 0.49098728551715626

Model Card of lmqg/mt5-small-squad-qg

This model is fine-tuned version of google/mt5-small for question generation task on the lmqg/qg_squad (dataset_name: default) via lmqg.

Overview

• Language model: google/mt5-small
• Language: en
• Training data: lmqg/qg_squad (default)
• Online Demo: https://autoqg.net/
• Repository: https://github.com/asahi417/lm-question-generation
• Paper: https://arxiv.org/abs/2210.03992

Usage

• With lmqg

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="lmqg/mt5-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/mt5-small-squad-qg")
output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

Evaluation

• Metric (Question Generation): raw metric file

	Score	Type	Dataset
BERTScore	90.01	default	lmqg/qg_squad
Bleu_1	54.07	default	lmqg/qg_squad
Bleu_2	37.62	default	lmqg/qg_squad
Bleu_3	28.18	default	lmqg/qg_squad
Bleu_4	21.65	default	lmqg/qg_squad
METEOR	23.83	default	lmqg/qg_squad
MoverScore	62.75	default	lmqg/qg_squad
ROUGE_L	48.95	default	lmqg/qg_squad

• Metrics (Question Generation, Out-of-Domain)

Dataset	Type	BERTScore	Bleu_4	METEOR	MoverScore	ROUGE_L	Link
lmqg/qg_dequad	default	73.53	0.0	4.81	50.37	1.56	link
lmqg/qg_esquad	default	74.94	0.59	6.02	50.62	5.21	link
lmqg/qg_frquad	default	72.91	1.71	8.24	50.96	15.84	link
lmqg/qg_itquad	default	72.6	0.54	5.89	50.23	5.01	link
lmqg/qg_jaquad	default	66.08	0.0	0.51	46.53	6.08	link
lmqg/qg_koquad	default	66.34	0.0	0.73	45.86	0.06	link
lmqg/qg_ruquad	default	70.89	0.0	1.78	49.1	0.99	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: None
• model: google/mt5-small
• max_length: 512
• max_length_output: 32
• epoch: 15
• batch: 64
• lr: 0.0005
• 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",
}