metadata
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_squad
pipeline_tag: text2text-generation
tags:
- question generation
- answer extraction
widget:
- text: >-
generate question: <hl> Beyonce <hl> further expanded her acting career,
starring as blues singer Etta James in the 2008 musical biopic, Cadillac
Records.
example_title: Question Generation Example 1
- text: >-
generate question: Beyonce further expanded her acting career, starring as
blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac
Records.
example_title: Question Generation Example 2
- text: >-
generate question: Beyonce further expanded her acting career, starring as
blues singer Etta James in the 2008 musical biopic, <hl> Cadillac Records
<hl> .
example_title: Question Generation Example 3
- text: >-
extract answers: <hl> Beyonce further expanded her acting career, starring
as blues singer Etta James in the 2008 musical biopic, Cadillac Records.
<hl> Her performance in the film received praise from critics, and she
garnered several nominations for her portrayal of James, including a
Satellite Award nomination for Best Supporting Actress, and a NAACP Image
Award nomination for Outstanding Supporting Actress.
example_title: Answer Extraction Example 1
- text: >-
extract answers: Beyonce further expanded her acting career, starring as
blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl>
Her performance in the film received praise from critics, and she garnered
several nominations for her portrayal of James, including a Satellite
Award nomination for Best Supporting Actress, and a NAACP Image Award
nomination for Outstanding Supporting Actress. <hl>
example_title: Answer Extraction Example 2
model-index:
- name: lmqg/flan-t5-base-squad-qg-ae
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 26.43
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 53.37
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 26.99
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 90.61
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 64.75
- name: >-
QAAlignedF1Score-BERTScore (Question & Answer Generation (with
Gold Answer))
type: >-
qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer
value: 93.31
- name: >-
QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold
Answer))
type: >-
qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer
value: 93.99
- name: >-
QAAlignedPrecision-BERTScore (Question & Answer Generation (with
Gold Answer))
type: >-
qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer
value: 92.65
- name: >-
QAAlignedF1Score-MoverScore (Question & Answer Generation (with
Gold Answer))
type: >-
qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer
value: 64.59
- name: >-
QAAlignedRecall-MoverScore (Question & Answer Generation (with
Gold Answer))
type: >-
qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer
value: 65.57
- name: >-
QAAlignedPrecision-MoverScore (Question & Answer Generation (with
Gold Answer))
type: >-
qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer
value: 63.7
- name: BLEU4 (Answer Extraction)
type: bleu4_answer_extraction
value: 35.6
- name: ROUGE-L (Answer Extraction)
type: rouge_l_answer_extraction
value: 68.47
- name: METEOR (Answer Extraction)
type: meteor_answer_extraction
value: 42.76
- name: BERTScore (Answer Extraction)
type: bertscore_answer_extraction
value: 91.28
- name: MoverScore (Answer Extraction)
type: moverscore_answer_extraction
value: 81.31
- name: AnswerF1Score (Answer Extraction)
type: answer_f1_score__answer_extraction
value: 68.88
- name: AnswerExactMatch (Answer Extraction)
type: answer_exact_match_answer_extraction
value: 57.39
Model Card of lmqg/flan-t5-base-squad-qg-ae
This model is fine-tuned version of google/flan-t5-base for question generation and answer extraction jointly on the lmqg/qg_squad (dataset_name: default) via lmqg.
Overview
- Language model: google/flan-t5-base
- Language: en
- Training data: lmqg/qg_squad (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="en", model="lmqg/flan-t5-base-squad-qg-ae")
# model prediction
question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")
- With
transformers
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/flan-t5-base-squad-qg-ae")
# answer extraction
answer = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
# question generation
question = pipe("extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.")
Evaluation
- Metric (Question Generation): raw metric file
| Score | Type | Dataset | |
|---|---|---|---|
| BERTScore | 90.61 | default | lmqg/qg_squad |
| Bleu_1 | 58.99 | default | lmqg/qg_squad |
| Bleu_2 | 42.92 | default | lmqg/qg_squad |
| Bleu_3 | 33.3 | default | lmqg/qg_squad |
| Bleu_4 | 26.43 | default | lmqg/qg_squad |
| METEOR | 26.99 | default | lmqg/qg_squad |
| MoverScore | 64.75 | default | lmqg/qg_squad |
| ROUGE_L | 53.37 | default | lmqg/qg_squad |
- Metric (Question & Answer Generation): raw metric file
| Score | Type | Dataset | |
|---|---|---|---|
| QAAlignedF1Score (BERTScore) | 93.31 | default | lmqg/qg_squad |
| QAAlignedF1Score (MoverScore) | 64.59 | default | lmqg/qg_squad |
| QAAlignedPrecision (BERTScore) | 92.65 | default | lmqg/qg_squad |
| QAAlignedPrecision (MoverScore) | 63.7 | default | lmqg/qg_squad |
| QAAlignedRecall (BERTScore) | 93.99 | default | lmqg/qg_squad |
| QAAlignedRecall (MoverScore) | 65.57 | default | lmqg/qg_squad |
- Metric (Answer Extraction): raw metric file
| Score | Type | Dataset | |
|---|---|---|---|
| AnswerExactMatch | 57.39 | default | lmqg/qg_squad |
| AnswerF1Score | 68.88 | default | lmqg/qg_squad |
| BERTScore | 91.28 | default | lmqg/qg_squad |
| Bleu_1 | 49.4 | default | lmqg/qg_squad |
| Bleu_2 | 44.53 | default | lmqg/qg_squad |
| Bleu_3 | 39.73 | default | lmqg/qg_squad |
| Bleu_4 | 35.6 | default | lmqg/qg_squad |
| METEOR | 42.76 | default | lmqg/qg_squad |
| MoverScore | 81.31 | default | lmqg/qg_squad |
| ROUGE_L | 68.47 | default | lmqg/qg_squad |
Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_squad
- dataset_name: default
- input_types: ['paragraph_answer', 'paragraph_sentence']
- output_types: ['question', 'answer']
- prefix_types: ['qg', 'ae']
- model: google/flan-t5-base
- max_length: 512
- max_length_output: 32
- epoch: 7
- batch: 8
- lr: 0.0001
- 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",
}