Model Card of lmqg/t5-small-tweetqa-qa

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

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="lmqg/t5-small-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/t5-small-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

Score Type Dataset
AnswerExactMatch 38.49 default lmqg/qg_tweetqa
AnswerF1Score 56.12 default lmqg/qg_tweetqa
BERTScore 92.19 default lmqg/qg_tweetqa
Bleu_1 45.54 default lmqg/qg_tweetqa
Bleu_2 37.38 default lmqg/qg_tweetqa
Bleu_3 29.91 default lmqg/qg_tweetqa
Bleu_4 23.73 default lmqg/qg_tweetqa
METEOR 27.89 default lmqg/qg_tweetqa
MoverScore 74.57 default lmqg/qg_tweetqa
ROUGE_L 49.86 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: t5-small
  • max_length: 512
  • max_length_output: 32
  • epoch: 7
  • batch: 64
  • lr: 0.0001
  • fp16: False
  • random_seed: 1
  • gradient_accumulation_steps: 1
  • label_smoothing: 0.0

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",
}
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Dataset used to train lmqg/t5-small-tweetqa-qa

Evaluation results

  • BLEU4 (Question Answering) on lmqg/qg_tweetqa
    self-reported
    23.730
  • ROUGE-L (Question Answering) on lmqg/qg_tweetqa
    self-reported
    49.860
  • METEOR (Question Answering) on lmqg/qg_tweetqa
    self-reported
    27.890
  • BERTScore (Question Answering) on lmqg/qg_tweetqa
    self-reported
    92.190
  • MoverScore (Question Answering) on lmqg/qg_tweetqa
    self-reported
    74.570
  • AnswerF1Score (Question Answering) on lmqg/qg_tweetqa
    self-reported
    56.120
  • AnswerExactMatch (Question Answering) on lmqg/qg_tweetqa
    self-reported
    38.490