metadata

license: cc-by-4.0
metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
language: en
datasets:
  - lmqg/qg_subjqa
pipeline_tag: text2text-generation
tags:
  - question generation
widget:
  - text: >-
      generate question: <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: >-
      generate question: 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: >-
      generate question: 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/bart-base-subjqa-vanilla-movies
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_subjqa
          type: movies
          args: movies
        metrics:
          - name: BLEU4
            type: bleu4
            value: 0.000001146037717373066
          - name: ROUGE-L
            type: rouge-l
            value: 0.20322881868683584
          - name: METEOR
            type: meteor
            value: 0.17164967153932714
          - name: BERTScore
            type: bertscore
            value: 0.9141060315169297
          - name: MoverScore
            type: moverscore
            value: 0.594084815785369

Model Card of `lmqg/bart-base-subjqa-vanilla-movies`

This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qg_subjqa (dataset_name: movies) via lmqg.

Please cite our paper if you use the model (https://arxiv.org/abs/2210.03992).


@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",
}

Overview

Language model: facebook/bart-base
Language: en
Training data: lmqg/qg_subjqa (movies)
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/bart-base-subjqa-vanilla-movies')
# model prediction
question = 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
# initialize model
pipe = pipeline("text2text-generation", 'lmqg/bart-base-subjqa-vanilla-movies')
# question generation
question = 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 Metrics

Metrics

Dataset	Type	BLEU4	ROUGE-L	METEOR	BERTScore	MoverScore	Link
lmqg/qg_subjqa	movies	0.0	0.203	0.172	0.914	0.594	link

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qg_subjqa
dataset_name: movies
input_types: ['paragraph_answer']
output_types: ['question']
prefix_types: ['qg']
model: facebook/bart-base
max_length: 512
max_length_output: 32
epoch: 1
batch: 8
lr: 5e-05
fp16: False
random_seed: 1
gradient_accumulation_steps: 16
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",
}

Model Card of lmqg/bart-base-subjqa-vanilla-movies