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: research-backup/bart-base-squad-qg-no-paragraph
    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: 23.86
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 51.43
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 25.18
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 90.7
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 63.85

Model Card of research-backup/bart-base-squad-qg-no-paragraph

This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qg_squad (dataset_name: default) via lmqg. This model is fine-tuned without pargraph information but only the sentence that contains the answer.

Overview

• Language model: facebook/bart-base
• 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="research-backup/bart-base-squad-qg-no-paragraph")

# 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", "research-backup/bart-base-squad-qg-no-paragraph")
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	90.7	default	lmqg/qg_squad
Bleu_1	55.85	default	lmqg/qg_squad
Bleu_2	39.85	default	lmqg/qg_squad
Bleu_3	30.44	default	lmqg/qg_squad
Bleu_4	23.86	default	lmqg/qg_squad
METEOR	25.18	default	lmqg/qg_squad
MoverScore	63.85	default	lmqg/qg_squad
ROUGE_L	51.43	default	lmqg/qg_squad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

• dataset_path: lmqg/qg_squad
• dataset_name: default
• input_types: ['sentence_answer']
• output_types: ['question']
• prefix_types: None
• model: facebook/bart-base
• max_length: 128
• max_length_output: 32
• epoch: 3
• batch: 64
• lr: 0.0001
• fp16: False
• random_seed: 1
• gradient_accumulation_steps: 2
• 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",
}