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: 0.33315782056958804
- name: ROUGE-L
type: rouge-l
value: 0.6223954899626488
- name: METEOR
type: meteor
value: 0.35426819977024687
- name: BERTScore
type: bertscore
value: 0.9458259980280204
- name: MoverScore
type: moverscore
value: 0.8006984647429997
- 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)
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",
}