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: lmqg/bart-base-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: 0.13271002553218747
          - name: ROUGE-L
            type: rouge-l
            value: 0.33394324628073685
          - name: METEOR
            type: meteor
            value: 0.25656735184843016
          - name: BERTScore
            type: bertscore
            value: 0.9118556745260903
          - name: MoverScore
            type: moverscore
            value: 0.6159359582631277
          - name: QAAlignedF1Score (BERTScore)
            type: qa_aligned_f1_score_bertscore
            value: 0.9149530689946653
          - name: QAAlignedF1Score (MoverScore)
            type: qa_aligned_f1_score_moverscore
            value: 0.6377628634079008

Model Card of `lmqg/bart-base-tweetqa-qag`

This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qag_tweetqa (dataset_name: default) via lmqg. This model is fine-tuned on the end-to-end question and answer generation.

Please cite our paper if you use the model (https://arxiv.org/abs/2210.03992).


@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",
}

Overview

Language model: facebook/bart-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/bart-base-tweetqa-qag')
# model prediction
question = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")

With transformers


from transformers import pipeline
# initialize model
pipe = pipeline("text2text-generation", 'lmqg/bart-base-tweetqa-qag')
# question generation
question = pipe('Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.')

Evaluation Metrics

Metrics

Dataset	Type	BLEU4	ROUGE-L	METEOR	BERTScore	MoverScore	Link
lmqg/qag_tweetqa	default	0.133	0.334	0.257	0.912	0.616	link

Metrics (QAG)

Dataset	Type	QA Aligned F1 Score (BERTScore)	QA Aligned F1 Score (MoverScore)	Link
lmqg/qag_tweetqa	default	0.915	0.638	link

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: facebook/bart-base
max_length: 256
max_length_output: 128
epoch: 15
batch: 32
lr: 5e-05
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",
}

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