metadata

license: cc-by-4.0
metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
language: en
datasets:
  - lmqg/qg_tweetqa
pipeline_tag: text2text-generation
tags:
  - question answering
widget:
  - text: >-
      question: What is a person called is practicing heresy?, context: Heresy
      is any provocative belief or theory that is strongly at variance with
      established beliefs or customs. A heretic is a proponent of such claims or
      beliefs. Heresy is distinct from both apostasy, which is the explicit
      renunciation of one's religion, principles or cause, and blasphemy, which
      is an impious utterance or action concerning God or sacred things.
    example_title: Question Answering Example 1
  - text: >-
      question: who created the post as we know it today?, context: 'So much of
      The Post is Ben,' Mrs. Graham said in 1994, three years after Bradlee
      retired as editor. 'He created it as we know it today.'— Ed O'Keefe
      (@edatpost) October 21, 2014
    example_title: Question Answering Example 2
model-index:
  - name: lmqg/t5-base-tweetqa-question-answering
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_tweetqa
          type: default
          args: default
        metrics:
          - name: BLEU4
            type: bleu4
            value: 33.3157820569588
          - name: ROUGE-L
            type: rouge-l
            value: 62.23954899626488
          - name: METEOR
            type: meteor
            value: 35.426819977024685
          - name: BERTScore
            type: bertscore
            value: 94.58259980280204
          - name: MoverScore
            type: moverscore
            value: 80.06984647429996
          - name: AnswerF1Score (Question Answering)
            type: answer_f1_score_question_answering
            value: 69.3966081647612
          - name: AnswerExactMatch (Question Answering)
            type: answer_exact_match_question_answering
            value: 52.28595178719867

Model Card of `lmqg/t5-base-tweetqa-question-answering`

This model is fine-tuned version of t5-base for question answering task on the lmqg/qg_tweetqa (dataset_name: default) via lmqg.

Overview

Language model: t5-base
Language: en
Training data: lmqg/qg_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-question-answering')

# model prediction
answers = model.answer_q(list_question="What is a person called is practicing heresy?", list_context=" Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.")

With transformers


from transformers import pipeline

pipe = pipeline("text2text-generation", 'lmqg/t5-base-tweetqa-question-answering')
output = pipe('question: What is a person called is practicing heresy?, context: Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.')

Evaluation Metrics

Metric (Question Answering)

	Score	Type	Dataset
AnswerExactMatch	52.286	default	lmqg/qg_tweetqa
AnswerF1Score	69.3966	default	lmqg/qg_tweetqa
BERTScore	94.5826	default	lmqg/qg_tweetqa
Bleu_1	57.0705	default	lmqg/qg_tweetqa
Bleu_2	48.1677	default	lmqg/qg_tweetqa
Bleu_3	39.7816	default	lmqg/qg_tweetqa
Bleu_4	33.3158	default	lmqg/qg_tweetqa
METEOR	35.4268	default	lmqg/qg_tweetqa
MoverScore	80.0698	default	lmqg/qg_tweetqa
ROUGE_L	62.2395	default	lmqg/qg_tweetqa

raw metric file

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qg_tweetqa
dataset_name: default
input_types: ['paragraph_question']
output_types: ['answer']
prefix_types: None
model: t5-base
max_length: 512
max_length_output: 32
epoch: 10
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",
}

Model Card of lmqg/t5-base-tweetqa-question-answering