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: >-
      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: research-backup/t5-base-tweetqa-qag-np
    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: 13.4
          - name: ROUGE-L (Question & Answer Generation)
            type: rouge_l_question_answer_generation
            value: 37.23
          - name: METEOR (Question & Answer Generation)
            type: meteor_question_answer_generation
            value: 31.14
          - name: BERTScore (Question & Answer Generation)
            type: bertscore_question_answer_generation
            value: 90.8
          - name: MoverScore (Question & Answer Generation)
            type: moverscore_question_answer_generation
            value: 62.26
          - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
            type: qa_aligned_f1_score_bertscore_question_answer_generation
            value: 92.4
          - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
            type: qa_aligned_recall_bertscore_question_answer_generation
            value: 92.03
          - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
            type: qa_aligned_precision_bertscore_question_answer_generation
            value: 92.78
          - name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
            type: qa_aligned_f1_score_moverscore_question_answer_generation
            value: 64.83
          - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
            type: qa_aligned_recall_moverscore_question_answer_generation
            value: 64.07
          - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
            type: qa_aligned_precision_moverscore_question_answer_generation
            value: 65.68

Model Card of `research-backup/t5-base-tweetqa-qag-np`

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. This model is fine-tuned without a task prefix.

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="research-backup/t5-base-tweetqa-qag-np")

# 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", "research-backup/t5-base-tweetqa-qag-np")
output = pipe("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	90.8	default	lmqg/qag_tweetqa
Bleu_1	40.49	default	lmqg/qag_tweetqa
Bleu_2	27.77	default	lmqg/qag_tweetqa
Bleu_3	19.18	default	lmqg/qag_tweetqa
Bleu_4	13.4	default	lmqg/qag_tweetqa
METEOR	31.14	default	lmqg/qag_tweetqa
MoverScore	62.26	default	lmqg/qag_tweetqa
QAAlignedF1Score (BERTScore)	92.4	default	lmqg/qag_tweetqa
QAAlignedF1Score (MoverScore)	64.83	default	lmqg/qag_tweetqa
QAAlignedPrecision (BERTScore)	92.78	default	lmqg/qag_tweetqa
QAAlignedPrecision (MoverScore)	65.68	default	lmqg/qag_tweetqa
QAAlignedRecall (BERTScore)	92.03	default	lmqg/qag_tweetqa
QAAlignedRecall (MoverScore)	64.07	default	lmqg/qag_tweetqa
ROUGE_L	37.23	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: None
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: 0.0

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",
}