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Model Card of research-backup/t5-small-squad-qg-no-paragraph

This model is fine-tuned version of t5-small for question generation task on the lmqg/qg_squad (dataset_name: default) via lmqg. This model is fine-tuned without pargraph information but only the sentence that contains the answer.

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="research-backup/t5-small-squad-qg-no-paragraph")

# model prediction
questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")
  • With transformers
from transformers import pipeline

pipe = pipeline("text2text-generation", "research-backup/t5-small-squad-qg-no-paragraph")
output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

Evaluation

Score Type Dataset
BERTScore 90.36 default lmqg/qg_squad
Bleu_1 55.39 default lmqg/qg_squad
Bleu_2 39.1 default lmqg/qg_squad
Bleu_3 29.7 default lmqg/qg_squad
Bleu_4 23.23 default lmqg/qg_squad
METEOR 24.8 default lmqg/qg_squad
MoverScore 63.18 default lmqg/qg_squad
ROUGE_L 50.18 default lmqg/qg_squad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

  • dataset_path: lmqg/qg_squad
  • dataset_name: default
  • input_types: ['sentence_answer']
  • output_types: ['question']
  • prefix_types: ['qg']
  • model: t5-small
  • max_length: 128
  • max_length_output: 32
  • epoch: 8
  • batch: 64
  • lr: 0.0001
  • fp16: False
  • random_seed: 1
  • gradient_accumulation_steps: 1
  • 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",
}
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Dataset used to train research-backup/t5-small-squad-qg-no-paragraph

Evaluation results