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Model Card of research-backup/t5-small-squadshifts-vanilla-new_wiki-qg

This model is fine-tuned version of t5-small for question generation task on the lmqg/qg_squadshifts (dataset_name: new_wiki) via lmqg.

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="research-backup/t5-small-squadshifts-vanilla-new_wiki-qg")

# 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-squadshifts-vanilla-new_wiki-qg")
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 83.08 new_wiki lmqg/qg_squadshifts
Bleu_1 6.9 new_wiki lmqg/qg_squadshifts
Bleu_2 2.75 new_wiki lmqg/qg_squadshifts
Bleu_3 1.38 new_wiki lmqg/qg_squadshifts
Bleu_4 0.81 new_wiki lmqg/qg_squadshifts
METEOR 8.26 new_wiki lmqg/qg_squadshifts
MoverScore 52.25 new_wiki lmqg/qg_squadshifts
ROUGE_L 8.85 new_wiki lmqg/qg_squadshifts

Training hyperparameters

The following hyperparameters were used during fine-tuning:

  • dataset_path: lmqg/qg_squadshifts
  • dataset_name: new_wiki
  • input_types: ['paragraph_answer']
  • output_types: ['question']
  • prefix_types: ['qg']
  • model: t5-small
  • max_length: 512
  • max_length_output: 32
  • epoch: 1
  • batch: 32
  • lr: 1e-05
  • fp16: False
  • random_seed: 1
  • gradient_accumulation_steps: 4
  • 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-squadshifts-vanilla-new_wiki-qg

Evaluation results

  • BLEU4 (Question Generation) on lmqg/qg_squadshifts
    self-reported
    0.810
  • ROUGE-L (Question Generation) on lmqg/qg_squadshifts
    self-reported
    8.850
  • METEOR (Question Generation) on lmqg/qg_squadshifts
    self-reported
    8.260
  • BERTScore (Question Generation) on lmqg/qg_squadshifts
    self-reported
    83.080
  • MoverScore (Question Generation) on lmqg/qg_squadshifts
    self-reported
    52.250