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model update
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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: research-backup/t5-small-subjqa-vanilla-electronics-qg
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_subjqa
          type: electronics
          args: electronics
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 0
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 2.38
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 2.64
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 52.85
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 49.57

Model Card of research-backup/t5-small-subjqa-vanilla-electronics-qg

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

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="research-backup/t5-small-subjqa-vanilla-electronics-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", "research-backup/t5-small-subjqa-vanilla-electronics-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 52.85 electronics lmqg/qg_subjqa
Bleu_1 2.89 electronics lmqg/qg_subjqa
Bleu_2 0.7 electronics lmqg/qg_subjqa
Bleu_3 0 electronics lmqg/qg_subjqa
Bleu_4 0 electronics lmqg/qg_subjqa
METEOR 2.64 electronics lmqg/qg_subjqa
MoverScore 49.57 electronics lmqg/qg_subjqa
ROUGE_L 2.38 electronics lmqg/qg_subjqa

Training hyperparameters

The following hyperparameters were used during fine-tuning:

  • dataset_path: lmqg/qg_subjqa
  • dataset_name: electronics
  • input_types: ['paragraph_answer']
  • output_types: ['question']
  • prefix_types: ['qg']
  • model: t5-small
  • max_length: 512
  • max_length_output: 32
  • epoch: 1
  • batch: 32
  • lr: 0.0001
  • fp16: False
  • random_seed: 1
  • gradient_accumulation_steps: 4
  • 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",
}