asahi417's picture
model update
f12423c
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
  - answer extraction
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
  - text: >-
      extract answers: <hl> Beyonce further expanded her acting career, starring
      as blues singer Etta James in the 2008 musical biopic, Cadillac Records.
      <hl> Her performance in the film received praise from critics, and she
      garnered several nominations for her portrayal of James, including a
      Satellite Award nomination for Best Supporting Actress, and a NAACP Image
      Award nomination for Outstanding Supporting Actress.
    example_title: Answer Extraction Example 1
  - text: >-
      extract answers: Beyonce further expanded her acting career, starring as
      blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl>
      Her performance in the film received praise from critics, and she garnered
      several nominations for her portrayal of James, including a Satellite
      Award nomination for Best Supporting Actress, and a NAACP Image Award
      nomination for Outstanding Supporting Actress. <hl>
    example_title: Answer Extraction Example 2
model-index:
  - name: lmqg/flan-t5-base-squad-qg-ae
    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: 26.43
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 53.37
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 26.99
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 90.61
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 64.75
          - name: >-
              QAAlignedF1Score-BERTScore (Question & Answer Generation (with
              Gold Answer))
            type: >-
              qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer
            value: 93.31
          - name: >-
              QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold
              Answer))
            type: >-
              qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer
            value: 93.99
          - name: >-
              QAAlignedPrecision-BERTScore (Question & Answer Generation (with
              Gold Answer))
            type: >-
              qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer
            value: 92.65
          - name: >-
              QAAlignedF1Score-MoverScore (Question & Answer Generation (with
              Gold Answer))
            type: >-
              qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer
            value: 64.59
          - name: >-
              QAAlignedRecall-MoverScore (Question & Answer Generation (with
              Gold Answer))
            type: >-
              qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer
            value: 65.57
          - name: >-
              QAAlignedPrecision-MoverScore (Question & Answer Generation (with
              Gold Answer))
            type: >-
              qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer
            value: 63.7
          - name: BLEU4 (Answer Extraction)
            type: bleu4_answer_extraction
            value: 35.6
          - name: ROUGE-L (Answer Extraction)
            type: rouge_l_answer_extraction
            value: 68.47
          - name: METEOR (Answer Extraction)
            type: meteor_answer_extraction
            value: 42.76
          - name: BERTScore (Answer Extraction)
            type: bertscore_answer_extraction
            value: 91.28
          - name: MoverScore (Answer Extraction)
            type: moverscore_answer_extraction
            value: 81.31
          - name: AnswerF1Score (Answer Extraction)
            type: answer_f1_score__answer_extraction
            value: 68.88
          - name: AnswerExactMatch (Answer Extraction)
            type: answer_exact_match_answer_extraction
            value: 57.39

Model Card of lmqg/flan-t5-base-squad-qg-ae

This model is fine-tuned version of google/flan-t5-base for question generation and answer extraction jointly on the lmqg/qg_squad (dataset_name: default) via lmqg.

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="lmqg/flan-t5-base-squad-qg-ae")

# model prediction
question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")
  • With transformers
from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/flan-t5-base-squad-qg-ae")

# answer extraction
answer = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

# question generation
question = pipe("extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.")

Evaluation

Score Type Dataset
BERTScore 90.61 default lmqg/qg_squad
Bleu_1 58.99 default lmqg/qg_squad
Bleu_2 42.92 default lmqg/qg_squad
Bleu_3 33.3 default lmqg/qg_squad
Bleu_4 26.43 default lmqg/qg_squad
METEOR 26.99 default lmqg/qg_squad
MoverScore 64.75 default lmqg/qg_squad
ROUGE_L 53.37 default lmqg/qg_squad
Score Type Dataset
QAAlignedF1Score (BERTScore) 93.31 default lmqg/qg_squad
QAAlignedF1Score (MoverScore) 64.59 default lmqg/qg_squad
QAAlignedPrecision (BERTScore) 92.65 default lmqg/qg_squad
QAAlignedPrecision (MoverScore) 63.7 default lmqg/qg_squad
QAAlignedRecall (BERTScore) 93.99 default lmqg/qg_squad
QAAlignedRecall (MoverScore) 65.57 default lmqg/qg_squad
Score Type Dataset
AnswerExactMatch 57.39 default lmqg/qg_squad
AnswerF1Score 68.88 default lmqg/qg_squad
BERTScore 91.28 default lmqg/qg_squad
Bleu_1 49.4 default lmqg/qg_squad
Bleu_2 44.53 default lmqg/qg_squad
Bleu_3 39.73 default lmqg/qg_squad
Bleu_4 35.6 default lmqg/qg_squad
METEOR 42.76 default lmqg/qg_squad
MoverScore 81.31 default lmqg/qg_squad
ROUGE_L 68.47 default lmqg/qg_squad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

  • dataset_path: lmqg/qg_squad
  • dataset_name: default
  • input_types: ['paragraph_answer', 'paragraph_sentence']
  • output_types: ['question', 'answer']
  • prefix_types: ['qg', 'ae']
  • model: google/flan-t5-base
  • max_length: 512
  • max_length_output: 32
  • epoch: 7
  • batch: 8
  • lr: 0.0001
  • 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",
}