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/t5-large-subjqa-vanilla-books
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_subjqa
          type: books
          args: books
        metrics:
          - name: BLEU4
            type: bleu4
            value: 4.518689535419543e-11
          - name: ROUGE-L
            type: rouge-l
            value: 0.06362608202312194
          - name: METEOR
            type: meteor
            value: 0.0463163512336036
          - name: BERTScore
            type: bertscore
            value: 0.8212118666833608
          - name: MoverScore
            type: moverscore
            value: 0.5029136160777198

Model Card of `lmqg/t5-large-subjqa-vanilla-books`

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

Please cite our paper if you use the model (TBA).


@inproceedings{ushio-etal-2022-generative,
    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration: {A} {U}nified {B}enchmark and {E}valuation",
    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: t5-large
Language: en
Training data: lmqg/qg_subjqa (books)
Online Demo: https://autoqg.net/
Repository: https://github.com/asahi417/lm-question-generation
Paper: TBA

Usage


from transformers import pipeline

model_path = 'lmqg/t5-large-subjqa-vanilla-books'
pipe = pipeline("text2text-generation", model_path)

# 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	books	0.0	0.064	0.046	0.821	0.503	link

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qg_subjqa
dataset_name: books
input_types: ['paragraph_answer']
output_types: ['question']
prefix_types: ['qg']
model: t5-large
max_length: 512
max_length_output: 32
epoch: 1
batch: 16
lr: 1e-05
fp16: False
random_seed: 1
gradient_accumulation_steps: 8
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: {A} {U}nified {B}enchmark and {E}valuation", 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/t5-large-subjqa-vanilla-books