metadata

license: cc-by-4.0
metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
language: de
datasets:
  - lmqg/qg_dequad
pipeline_tag: text2text-generation
tags:
  - question generation
widget:
  - text: >-
      Empfangs- und Sendeantenne sollen in ihrer Polarisation übereinstimmen,
      andernfalls <hl> wird die Signalübertragung stark gedämpft. <hl>
    example_title: Question Generation Example 1
  - text: >-
      das erste weltweit errichtete Hermann Brehmer <hl> 1855 <hl> im
      niederschlesischen ''Görbersdorf'' (heute Sokołowsko, Polen).
    example_title: Question Generation Example 2
  - text: >-
      Er muss Zyperngrieche sein und wird direkt für <hl> fünf Jahre <hl>
      gewählt (Art. 43 Abs. 1 der Verfassung) und verfügt über weitreichende
      Exekutivkompetenzen.
    example_title: Question Generation Example 3
model-index:
  - name: lmqg/mt5-base-dequad-qg
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_dequad
          type: default
          args: default
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 0.87
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 11.1
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 13.65
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 80.39
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 55.73

Model Card of `lmqg/mt5-base-dequad-qg`

This model is fine-tuned version of google/mt5-base for question generation task on the lmqg/qg_dequad (dataset_name: default) via lmqg.

Overview

Language model: google/mt5-base
Language: de
Training data: lmqg/qg_dequad (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="de", model="lmqg/mt5-base-dequad-qg")

# model prediction
questions = model.generate_q(list_context="das erste weltweit errichtete Hermann Brehmer 1855 im niederschlesischen ''Görbersdorf'' (heute Sokołowsko, Polen).", list_answer="1855")

With transformers

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mt5-base-dequad-qg")
output = pipe("Empfangs- und Sendeantenne sollen in ihrer Polarisation übereinstimmen, andernfalls <hl> wird die Signalübertragung stark gedämpft. <hl>")

Evaluation

Metric (Question Generation): raw metric file

	Score	Type	Dataset
BERTScore	80.39	default	lmqg/qg_dequad
Bleu_1	10.85	default	lmqg/qg_dequad
Bleu_2	4.61	default	lmqg/qg_dequad
Bleu_3	2.06	default	lmqg/qg_dequad
Bleu_4	0.87	default	lmqg/qg_dequad
METEOR	13.65	default	lmqg/qg_dequad
MoverScore	55.73	default	lmqg/qg_dequad
ROUGE_L	11.1	default	lmqg/qg_dequad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qg_dequad
dataset_name: default
input_types: ['paragraph_answer']
output_types: ['question']
prefix_types: None
model: google/mt5-base
max_length: 512
max_length_output: 32
epoch: 17
batch: 4
lr: 0.0005
fp16: False
random_seed: 1
gradient_accumulation_steps: 16
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",
}