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/t5-small-tweetqa-qag-np
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qag_tweetqa
          type: default
          args: default
        metrics:
          - name: BLEU4
            type: bleu4
            value: 0.1071170387718974
          - name: ROUGE-L
            type: rouge-l
            value: 0.34768502761194764
          - name: METEOR
            type: meteor
            value: 0.2780003860872953
          - name: BERTScore
            type: bertscore
            value: 0.8947686245265588
          - name: MoverScore
            type: moverscore
            value: 0.6053481203449237

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

This model is fine-tuned version of t5-small 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: 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-np')
# model prediction
question = model.generate_qa(list_context=["William Turner was an English painter who specialised in watercolour landscapes"], list_answer=["William Turner"])

With transformers


from transformers import pipeline
# initialize model
pipe = pipeline("text2text-generation", 'lmqg/t5-small-tweetqa-qag-np')
# 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	0.107	0.348	0.278	0.895	0.605	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: t5-small
max_length: 256
max_length_output: 128
epoch: 16
batch: 64
lr: 0.0001
fp16: False
random_seed: 1
gradient_accumulation_steps: 1
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/t5-small-tweetqa-qag-np