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/bart-base-tweetqa-qa
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_tweetqa
          type: default
          args: default
        metrics:
          - name: BLEU4 (Question Answering)
            type: bleu4_question_answering
            value: 33.57
          - name: ROUGE-L (Question Answering)
            type: rouge_l_question_answering
            value: 58.37
          - name: METEOR (Question Answering)
            type: meteor_question_answering
            value: 32.39
          - name: BERTScore (Question Answering)
            type: bertscore_question_answering
            value: 93.84
          - name: MoverScore (Question Answering)
            type: moverscore_question_answering
            value: 78.67
          - name: AnswerF1Score (Question Answering)
            type: answer_f1_score__question_answering
            value: 64.79
          - name: AnswerExactMatch (Question Answering)
            type: answer_exact_match_question_answering
            value: 48.38

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

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

Overview

Language model: facebook/bart-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/bart-base-tweetqa-qa")

# 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/bart-base-tweetqa-qa")
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

Metric (Question Answering): raw metric file

	Score	Type	Dataset
AnswerExactMatch	48.38	default	lmqg/qg_tweetqa
AnswerF1Score	64.79	default	lmqg/qg_tweetqa
BERTScore	93.84	default	lmqg/qg_tweetqa
Bleu_1	54.68	default	lmqg/qg_tweetqa
Bleu_2	46.42	default	lmqg/qg_tweetqa
Bleu_3	38.97	default	lmqg/qg_tweetqa
Bleu_4	33.57	default	lmqg/qg_tweetqa
METEOR	32.39	default	lmqg/qg_tweetqa
MoverScore	78.67	default	lmqg/qg_tweetqa
ROUGE_L	58.37	default	lmqg/qg_tweetqa

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: facebook/bart-base
max_length: 512
max_length_output: 32
epoch: 3
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",
}