metadata
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: es
datasets:
- lmqg/qg_esquad
pipeline_tag: text2text-generation
tags:
- question generation
- answer extraction
widget:
- text: >-
generate question: del <hl> Ministerio de Desarrollo Urbano <hl> ,
Gobierno de la India.
example_title: Question Generation Example 1
- text: >-
generate question: a <hl> noviembre <hl> , que es también la estación
lluviosa.
example_title: Question Generation Example 2
- text: >-
generate question: como <hl> el gobierno de Abbott <hl> que asumió el
cargo el 18 de septiembre de 2013.
example_title: Question Generation Example 3
- text: >-
extract answers: <hl> En la diáspora somalí, múltiples eventos islámicos
de recaudación de fondos se llevan a cabo cada año en ciudades como
Birmingham, Londres, Toronto y Minneapolis, donde los académicos y
profesionales somalíes dan conferencias y responden preguntas de la
audiencia. <hl> El propósito de estos eventos es recaudar dinero para
nuevas escuelas o universidades en Somalia, para ayudar a los somalíes que
han sufrido como consecuencia de inundaciones y / o sequías, o para reunir
fondos para la creación de nuevas mezquitas como.
example_title: Answer Extraction Example 1
- text: >-
extract answers: <hl> Los estudiosos y los histori a dores están divididos
en cuanto a qué evento señala el final de la era helenística. <hl> El
período helenístico se puede ver que termina con la conquista final del
corazón griego por Roma en 146 a. C. tras la guerra aquea, con la derrota
final del reino ptolemaico en la batalla de Actium en 31 a. Helenístico se
distingue de helénico en que el primero abarca toda la esfera de
influencia griega antigua directa, mientras que el segundo se refiere a la
propia Grecia.
example_title: Answer Extraction Example 2
model-index:
- name: lmqg/mt5-small-esquad-qg-ae
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_esquad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 8.79
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 23.13
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 21.66
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 83.39
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 58.34
- name: BLEU4 (Question & Answer Generation (with Gold Answer))
type: bleu4_question_answer_generation_with_gold_answer
value: 1.73
- name: ROUGE-L (Question & Answer Generation (with Gold Answer))
type: rouge_l_question_answer_generation_with_gold_answer
value: 14.86
- name: METEOR (Question & Answer Generation (with Gold Answer))
type: meteor_question_answer_generation_with_gold_answer
value: 21.82
- name: BERTScore (Question & Answer Generation (with Gold Answer))
type: bertscore_question_answer_generation_with_gold_answer
value: 68.93
- name: MoverScore (Question & Answer Generation (with Gold Answer))
type: moverscore_question_answer_generation_with_gold_answer
value: 51.59
- name: >-
QAAlignedF1Score-BERTScore (Question & Answer Generation (with
Gold Answer))
type: >-
qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer
value: 79.06
- name: >-
QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold
Answer))
type: >-
qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer
value: 81.94
- name: >-
QAAlignedPrecision-BERTScore (Question & Answer Generation (with
Gold Answer))
type: >-
qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer
value: 76.46
- name: >-
QAAlignedF1Score-MoverScore (Question & Answer Generation (with
Gold Answer))
type: >-
qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer
value: 54.49
- name: >-
QAAlignedRecall-MoverScore (Question & Answer Generation (with
Gold Answer))
type: >-
qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer
value: 56.21
- name: >-
QAAlignedPrecision-MoverScore (Question & Answer Generation (with
Gold Answer))
type: >-
qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer
value: 52.96
- name: BLEU4 (Answer Extraction)
type: bleu4_answer_extraction
value: 23.89
- name: ROUGE-L (Answer Extraction)
type: rouge_l_answer_extraction
value: 48.58
- name: METEOR (Answer Extraction)
type: meteor_answer_extraction
value: 43.11
- name: BERTScore (Answer Extraction)
type: bertscore_answer_extraction
value: 89.77
- name: MoverScore (Answer Extraction)
type: moverscore_answer_extraction
value: 80.64
- name: AnswerF1Score (Answer Extraction)
type: answer_f1_score__answer_extraction
value: 75.31
- name: AnswerExactMatch (Answer Extraction)
type: answer_exact_match_answer_extraction
value: 57.63
Model Card of lmqg/mt5-small-esquad-qg-ae
This model is fine-tuned version of google/mt5-small for question generation and answer extraction jointly on the lmqg/qg_esquad (dataset_name: default) via lmqg.
Overview
- Language model: google/mt5-small
- Language: es
- Training data: lmqg/qg_esquad (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="es", model="lmqg/mt5-small-esquad-qg-ae")
# model prediction
question_answer_pairs = model.generate_qa("a noviembre , que es también la estación lluviosa.")
- With
transformers
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mt5-small-esquad-qg-ae")
# answer extraction
answer = pipe("generate question: del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.")
# question generation
question = pipe("extract answers: <hl> En la diáspora somalí, múltiples eventos islámicos de recaudación de fondos se llevan a cabo cada año en ciudades como Birmingham, Londres, Toronto y Minneapolis, donde los académicos y profesionales somalíes dan conferencias y responden preguntas de la audiencia. <hl> El propósito de estos eventos es recaudar dinero para nuevas escuelas o universidades en Somalia, para ayudar a los somalíes que han sufrido como consecuencia de inundaciones y / o sequías, o para reunir fondos para la creación de nuevas mezquitas como.")
Evaluation
- Metric (Question Generation): raw metric file
| Score | Type | Dataset | |
|---|---|---|---|
| BERTScore | 83.39 | default | lmqg/qg_esquad |
| Bleu_1 | 24.5 | default | lmqg/qg_esquad |
| Bleu_2 | 16.48 | default | lmqg/qg_esquad |
| Bleu_3 | 11.83 | default | lmqg/qg_esquad |
| Bleu_4 | 8.79 | default | lmqg/qg_esquad |
| METEOR | 21.66 | default | lmqg/qg_esquad |
| MoverScore | 58.34 | default | lmqg/qg_esquad |
| ROUGE_L | 23.13 | default | lmqg/qg_esquad |
- Metric (Question & Answer Generation): raw metric file
| Score | Type | Dataset | |
|---|---|---|---|
| BERTScore | 68.93 | default | lmqg/qg_esquad |
| Bleu_1 | 10.52 | default | lmqg/qg_esquad |
| Bleu_2 | 5.19 | default | lmqg/qg_esquad |
| Bleu_3 | 2.82 | default | lmqg/qg_esquad |
| Bleu_4 | 1.73 | default | lmqg/qg_esquad |
| METEOR | 21.82 | default | lmqg/qg_esquad |
| MoverScore | 51.59 | default | lmqg/qg_esquad |
| QAAlignedF1Score (BERTScore) | 79.06 | default | lmqg/qg_esquad |
| QAAlignedF1Score (MoverScore) | 54.49 | default | lmqg/qg_esquad |
| QAAlignedPrecision (BERTScore) | 76.46 | default | lmqg/qg_esquad |
| QAAlignedPrecision (MoverScore) | 52.96 | default | lmqg/qg_esquad |
| QAAlignedRecall (BERTScore) | 81.94 | default | lmqg/qg_esquad |
| QAAlignedRecall (MoverScore) | 56.21 | default | lmqg/qg_esquad |
| ROUGE_L | 14.86 | default | lmqg/qg_esquad |
- Metric (Answer Extraction): raw metric file
| Score | Type | Dataset | |
|---|---|---|---|
| AnswerExactMatch | 57.63 | default | lmqg/qg_esquad |
| AnswerF1Score | 75.31 | default | lmqg/qg_esquad |
| BERTScore | 89.77 | default | lmqg/qg_esquad |
| Bleu_1 | 35.18 | default | lmqg/qg_esquad |
| Bleu_2 | 30.48 | default | lmqg/qg_esquad |
| Bleu_3 | 26.92 | default | lmqg/qg_esquad |
| Bleu_4 | 23.89 | default | lmqg/qg_esquad |
| METEOR | 43.11 | default | lmqg/qg_esquad |
| MoverScore | 80.64 | default | lmqg/qg_esquad |
| ROUGE_L | 48.58 | default | lmqg/qg_esquad |
Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_esquad
- dataset_name: default
- input_types: ['paragraph_answer', 'paragraph_sentence']
- output_types: ['question', 'answer']
- prefix_types: ['qg', 'ae']
- model: google/mt5-small
- max_length: 512
- max_length_output: 32
- epoch: 5
- batch: 16
- lr: 0.001
- 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",
}