---

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-small-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: 23.73
    • name: ROUGE-L (Question Answering) type: rouge_l_question_answering value: 49.86
    • name: METEOR (Question Answering) type: meteor_question_answering value: 27.89
    • name: BERTScore (Question Answering) type: bertscore_question_answering value: 92.19
    • name: MoverScore (Question Answering) type: moverscore_question_answering value: 74.57
    • name: AnswerF1Score (Question Answering) type: answer_f1_score__question_answering value: 56.12
    • name: AnswerExactMatch (Question Answering) type: answer_exact_match_question_answering value: 38.49

---

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

• Language model: t5-small
• 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-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

• Metric (Question Answering): 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-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",
}