README.md

metadata

license: cc-by-4.0
metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
language: en
datasets:
  - lmqg/qag_tweetqa
pipeline_tag: text2text-generation
tags:
  - questions and answers generation
widget:
  - text: >-
      generate question and answer: Beyonce further expanded her acting career,
      starring as blues singer Etta James in the 2008 musical biopic, Cadillac
      Records.
    example_title: Questions & Answers Generation Example 1
model-index:
  - name: lmqg/t5-base-tweetqa-qag
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qag_tweetqa
          type: default
          args: default
        metrics:
          - name: BLEU4 (Question & Answer Generation)
            type: bleu4_question_answer_generation
            value: 12.93
          - name: ROUGE-L (Question & Answer Generation)
            type: rouge_l_question_answer_generation
            value: 36.54
          - name: METEOR (Question & Answer Generation)
            type: meteor_question_answer_generation
            value: 30.35
          - name: BERTScore (Question & Answer Generation)
            type: bertscore_question_answer_generation
            value: 90.55
          - name: MoverScore (Question & Answer Generation)
            type: moverscore_question_answer_generation
            value: 61.82
          - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
            type: qa_aligned_f1_score_bertscore_question_answer_generation
            value: 92.37
          - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
            type: qa_aligned_recall_bertscore_question_answer_generation
            value: 92.01
          - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
            type: qa_aligned_precision_bertscore_question_answer_generation
            value: 92.75
          - name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
            type: qa_aligned_f1_score_moverscore_question_answer_generation
            value: 64.63
          - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
            type: qa_aligned_recall_moverscore_question_answer_generation
            value: 63.85
          - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
            type: qa_aligned_precision_moverscore_question_answer_generation
            value: 65.5

Model Card of lmqg/t5-base-tweetqa-qag

This model is fine-tuned version of t5-base for question & answer pair generation task on the lmqg/qag_tweetqa (dataset_name: default) via lmqg.

Overview

• Language model: t5-base
• Language: en
• Training data: lmqg/qag_tweetqa (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="lmqg/t5-base-tweetqa-qag")

# 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/t5-base-tweetqa-qag")
output = pipe("generate question and answer: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

Evaluation

• Metric (Question & Answer Generation): raw metric file

	Score	Type	Dataset
BERTScore	90.55	default	lmqg/qag_tweetqa
Bleu_1	39.29	default	lmqg/qag_tweetqa
Bleu_2	26.69	default	lmqg/qag_tweetqa
Bleu_3	18.4	default	lmqg/qag_tweetqa
Bleu_4	12.93	default	lmqg/qag_tweetqa
METEOR	30.35	default	lmqg/qag_tweetqa
MoverScore	61.82	default	lmqg/qag_tweetqa
QAAlignedF1Score (BERTScore)	92.37	default	lmqg/qag_tweetqa
QAAlignedF1Score (MoverScore)	64.63	default	lmqg/qag_tweetqa
QAAlignedPrecision (BERTScore)	92.75	default	lmqg/qag_tweetqa
QAAlignedPrecision (MoverScore)	65.5	default	lmqg/qag_tweetqa
QAAlignedRecall (BERTScore)	92.01	default	lmqg/qag_tweetqa
QAAlignedRecall (MoverScore)	63.85	default	lmqg/qag_tweetqa
ROUGE_L	36.54	default	lmqg/qag_tweetqa

Training hyperparameters

The following hyperparameters were used during fine-tuning:

• dataset_path: lmqg/qag_tweetqa
• dataset_name: default
• input_types: ['paragraph']
• output_types: ['questions_answers']
• prefix_types: ['qag']
• model: t5-base
• max_length: 256
• max_length_output: 128
• epoch: 15
• batch: 32
• 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",
}