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-small-tweetqa-qag
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qag_tweetqa
          type: default
          args: default
        metrics:
          - name: BLEU4
            type: bleu4
            value: 10.08
          - name: ROUGE-L
            type: rouge-l
            value: 34.19
          - name: METEOR
            type: meteor
            value: 28.02
          - name: BERTScore
            type: bertscore
            value: 89.64
          - name: MoverScore
            type: moverscore
            value: 60.47
          - name: QAAlignedF1Score (BERTScore)
            type: qa_aligned_f1_score_bertscore
            value: 91.42
          - name: QAAlignedRecall (BERTScore)
            type: qa_aligned_recall_bertscore
            value: 90.98
          - name: QAAlignedPrecision (BERTScore)
            type: qa_aligned_precision_bertscore
            value: 91.89
          - name: QAAlignedF1Score (MoverScore)
            type: qa_aligned_f1_score_moverscore
            value: 63.08
          - name: QAAlignedRecall (MoverScore)
            type: qa_aligned_recall_moverscore
            value: 62.16
          - name: QAAlignedPrecision (MoverScore)
            type: qa_aligned_precision_moverscore
            value: 64.08

Model Card of `lmqg/t5-small-tweetqa-qag`

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

Overview

Language model: t5-small
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-small-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-small-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	89.64	default	lmqg/qag_tweetqa
Bleu_1	35.53	default	lmqg/qag_tweetqa
Bleu_2	22.94	default	lmqg/qag_tweetqa
Bleu_3	15.11	default	lmqg/qag_tweetqa
Bleu_4	10.08	default	lmqg/qag_tweetqa
METEOR	28.02	default	lmqg/qag_tweetqa
MoverScore	60.47	default	lmqg/qag_tweetqa
QAAlignedF1Score (BERTScore)	91.42	default	lmqg/qag_tweetqa
QAAlignedF1Score (MoverScore)	63.08	default	lmqg/qag_tweetqa
QAAlignedPrecision (BERTScore)	91.89	default	lmqg/qag_tweetqa
QAAlignedPrecision (MoverScore)	64.08	default	lmqg/qag_tweetqa
QAAlignedRecall (BERTScore)	90.98	default	lmqg/qag_tweetqa
QAAlignedRecall (MoverScore)	62.16	default	lmqg/qag_tweetqa
ROUGE_L	34.19	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-small
max_length: 256
max_length_output: 128
epoch: 14
batch: 64
lr: 0.0001
fp16: False
random_seed: 1
gradient_accumulation_steps: 1
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",
}