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
- name: BLEU4 (Question & Answer Generation (with Gold Answer))
type: bleu4_question_answer_generation_with_gold_answer
value: 0.1
- name: ROUGE-L (Question & Answer Generation (with Gold Answer))
type: rouge_l_question_answer_generation_with_gold_answer
value: 16.04
- name: METEOR (Question & Answer Generation (with Gold Answer))
type: meteor_question_answer_generation_with_gold_answer
value: 20.11
- name: BERTScore (Question & Answer Generation (with Gold Answer))
type: bertscore_question_answer_generation_with_gold_answer
value: 74.64
- name: MoverScore (Question & Answer Generation (with Gold Answer))
type: moverscore_question_answer_generation_with_gold_answer
value: 52.95
- name: >-
QAAlignedF1Score-BERTScore (Question & Answer Generation (with
Gold Answer)) [Gold Answer]
type: >-
qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer
value: 90.63
- name: >-
QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold
Answer)) [Gold Answer]
type: >-
qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer
value: 90.61
- name: >-
QAAlignedPrecision-BERTScore (Question & Answer Generation (with
Gold Answer)) [Gold Answer]
type: >-
qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer
value: 90.65
- name: >-
QAAlignedF1Score-MoverScore (Question & Answer Generation (with
Gold Answer)) [Gold Answer]
type: >-
qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer
value: 65.32
- name: >-
QAAlignedRecall-MoverScore (Question & Answer Generation (with
Gold Answer)) [Gold Answer]
type: >-
qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer
value: 65.3
- name: >-
QAAlignedPrecision-MoverScore (Question & Answer Generation (with
Gold Answer)) [Gold Answer]
type: >-
qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_gold_answer
value: 65.34
- name: BLEU4 (Question & Answer Generation)
type: bleu4_question_answer_generation
value: 0.87
- name: ROUGE-L (Question & Answer Generation)
type: rouge_l_question_answer_generation
value: 15.67
- name: METEOR (Question & Answer Generation)
type: meteor_question_answer_generation
value: 19.57
- name: BERTScore (Question & Answer Generation)
type: bertscore_question_answer_generation
value: 69.82
- name: MoverScore (Question & Answer Generation)
type: moverscore_question_answer_generation
value: 52.32
- name: >-
QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold
Answer]
type: >-
qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
value: 76.86
- name: >-
QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold
Answer]
type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
value: 77.55
- name: >-
QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold
Answer]
type: >-
qa_aligned_precision_bertscore_question_answer_generation_gold_answer
value: 76.28
- name: >-
QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold
Answer]
type: >-
qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
value: 52.96
- name: >-
QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold
Answer]
type: >-
qa_aligned_recall_moverscore_question_answer_generation_gold_answer
value: 53.06
- name: >-
QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold
Answer]
type: >-
qa_aligned_precision_moverscore_question_answer_generation_gold_answer
value: 52.93
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 |
- Metric (Question & Answer Generation, Reference Answer): Each question is generated from the gold answer. raw metric file
| Score | Type | Dataset | |
|---|---|---|---|
| BERTScore | 74.64 | default | lmqg/qg_dequad |
| Bleu_1 | 14.48 | default | lmqg/qg_dequad |
| Bleu_2 | 6.53 | default | lmqg/qg_dequad |
| Bleu_3 | 0.61 | default | lmqg/qg_dequad |
| Bleu_4 | 0.1 | default | lmqg/qg_dequad |
| METEOR | 20.11 | default | lmqg/qg_dequad |
| MoverScore | 52.95 | default | lmqg/qg_dequad |
| QAAlignedF1Score (BERTScore) | 90.63 | default | lmqg/qg_dequad |
| QAAlignedF1Score (MoverScore) | 65.32 | default | lmqg/qg_dequad |
| QAAlignedPrecision (BERTScore) | 90.65 | default | lmqg/qg_dequad |
| QAAlignedPrecision (MoverScore) | 65.34 | default | lmqg/qg_dequad |
| QAAlignedRecall (BERTScore) | 90.61 | default | lmqg/qg_dequad |
| QAAlignedRecall (MoverScore) | 65.3 | default | lmqg/qg_dequad |
| ROUGE_L | 16.04 | default | lmqg/qg_dequad |
- Metric (Question & Answer Generation, Pipeline Approach): Each question is generated on the answer generated by
lmqg/mt5-base-dequad-ae. raw metric file
| Score | Type | Dataset | |
|---|---|---|---|
| BERTScore | 69.82 | default | lmqg/qg_dequad |
| Bleu_1 | 13.47 | default | lmqg/qg_dequad |
| Bleu_2 | 6.32 | default | lmqg/qg_dequad |
| Bleu_3 | 2.03 | default | lmqg/qg_dequad |
| Bleu_4 | 0.87 | default | lmqg/qg_dequad |
| METEOR | 19.57 | default | lmqg/qg_dequad |
| MoverScore | 52.32 | default | lmqg/qg_dequad |
| QAAlignedF1Score (BERTScore) | 76.86 | default | lmqg/qg_dequad |
| QAAlignedF1Score (MoverScore) | 52.96 | default | lmqg/qg_dequad |
| QAAlignedPrecision (BERTScore) | 76.28 | default | lmqg/qg_dequad |
| QAAlignedPrecision (MoverScore) | 52.93 | default | lmqg/qg_dequad |
| QAAlignedRecall (BERTScore) | 77.55 | default | lmqg/qg_dequad |
| QAAlignedRecall (MoverScore) | 53.06 | default | lmqg/qg_dequad |
| ROUGE_L | 15.67 | 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",
}