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EN
T5 Base Squad Qg
An English question generation model based on the T5-base architecture, specifically optimized for the SQuAD dataset, capable of generating relevant questions from given text and answers.
Downloads 309
Release Time : 3/2/2022
Model Overview
This model is a text-to-text generation model primarily used to generate relevant questions from given text passages and highlighted answers. It is trained on the SQuAD dataset and suitable for educational and Q&A system scenarios.
Model Features
High-Quality Question Generation
Performs well on the SQuAD dataset with a BLEU4 score of 26.13 and ROUGE-L score of 53.33
Answer-Aware Generation
Capable of generating relevant questions based on highlighted answers in the text
Multi-Metric Evaluation
Supports various evaluation metrics including BLEU, METEOR, ROUGE-L, BERTScore, and MoverScore
Model Capabilities
Text Generation
Question Generation
Answer-Aware Question Generation
Use Cases
Education
Automated Reading Comprehension Question Generation
Automatically generates reading comprehension questions based on textbook content
Achieves a BERTScore of 90.6 on the SQuAD dataset
Q&A Systems
Q&A Data Augmentation
Generates training data for Q&A systems
QAAlignedF1Score-BERTScore reaches 95.42
๐ Model Card of lmqg/t5-base-squad-qg
This model is a fine - tuned version of [t5 - base](https://huggingface.co/t5 - base) for the question generation task on the lmqg/qg_squad (dataset_name: default) via [lmqg](https://github.com/asahi417/lm - question - generation). It aims to effectively generate questions based on given text, providing a useful tool for natural language processing applications.
๐ Quick Start
Overview
| Property | Details |
|---|---|
| Language model | [t5 - base](https://huggingface.co/t5 - base) |
| Language | en |
| Training Data | lmqg/qg_squad (default) |
| Online Demo | https://autoqg.net/ |
| Repository | [https://github.com/asahi417/lm - question - generation](https://github.com/asahi417/lm - question - generation) |
| Paper | https://arxiv.org/abs/2210.03992 |
๐ป Usage Examples
Basic Usage
- With [
lmqg](https://github.com/asahi417/lm - question - generation#lmqg - language - model - for - question - generation -)
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="en", model="lmqg/t5-base-squad-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", "lmqg/t5-base-squad-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.")
๐ Documentation
Metrics
The model is evaluated using the following metrics:
- bleu4
- meteor
- rouge - l
- bertscore
- moverscore
Widget Examples
- Question Generation Example 1:
- Input: "generate question:
Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
- Input: "generate question:
- Question Generation Example 2:
- Input: "generate question: Beyonce further expanded her acting career, starring as blues singer
Etta James in the 2008 musical biopic, Cadillac Records."
- Input: "generate question: Beyonce further expanded her acting career, starring as blues singer
- Question Generation Example 3:
- Input: "generate question: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic,
Cadillac Records ."
- Input: "generate question: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic,
Model Index
The model lmqg/t5 - base - squad - qg has the following evaluation results:
- Dataset: lmqg/qg_squad
- Metrics:
- BLEU4 (Question Generation): 26.13
- ROUGE - L (Question Generation): 53.33
- METEOR (Question Generation): 26.97
- BERTScore (Question Generation): 90.6
- MoverScore (Question Generation): 64.74
- QAAlignedF1Score - BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]: 95.42
- QAAlignedRecall - BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]: 95.37
- QAAlignedPrecision - BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]: 95.48
- QAAlignedF1Score - MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]: 70.63
- QAAlignedRecall - MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]: 70.34
- QAAlignedPrecision - MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]: 70.92
- QAAlignedF1Score - BERTScore (Question & Answer Generation) [Gold Answer]: 92.75
- QAAlignedRecall - BERTScore (Question & Answer Generation) [Gold Answer]: 92.93
- QAAlignedPrecision - BERTScore (Question & Answer Generation) [Gold Answer]: 92.59
- QAAlignedF1Score - MoverScore (Question & Answer Generation) [Gold Answer]: 64.36
- QAAlignedRecall - MoverScore (Question & Answer Generation) [Gold Answer]: 64.35
- QAAlignedPrecision - MoverScore (Question & Answer Generation) [Gold Answer]: 64.45
- Metrics:
- Dataset: lmqg/qg_squadshifts (amazon)
- Metrics:
- BLEU4 (Question Generation): 0.06566094160179252
- ROUGE - L (Question Generation): 0.24807913266651793
- METEOR (Question Generation): 0.22371955880948402
- BERTScore (Question Generation): 0.9075296597429775
- MoverScore (Question Generation): 0.6080134772590127
- Metrics:
- Dataset: lmqg/qg_squadshifts (new_wiki)
- Metrics:
- BLEU4 (Question Generation): 0.11090197883325803
- ROUGE - L (Question Generation): 0.2958807755982971
- METEOR (Question Generation): 0.2723283879163309
- BERTScore (Question Generation): 0.9301888817677253
- MoverScore (Question Generation): 0.6596737223946099
- Metrics:
- Dataset: lmqg/qg_squadshifts (nyt)
- Metrics:
- BLEU4 (Question Generation): 0.07770444680489934
- ROUGE - L (Question Generation): 0.24562552942523097
- METEOR (Question Generation): 0.2516102599911737
- BERTScore (Question Generation): 0.9220106686608106
- MoverScore (Question Generation): 0.638293725604755
- Metrics:
- Dataset: lmqg/qg_squadshifts (reddit)
- Metrics:
- BLEU4 (Question Generation): 0.05681866334465563
- ROUGE - L (Question Generation): 0.21961287790760073
- METEOR (Question Generation): 0.2129793223231344
- BERTScore (Question Generation): 0.9058513802527968
- MoverScore (Question Generation): 0.6023495282031547
- Metrics:
- Dataset: lmqg/qg_subjqa (books)
- Metrics:
- BLEU4 (Question Generation): 0.004910619965406665
- ROUGE - L (Question Generation): 0.09444487769816154
- METEOR (Question Generation): 0.13509168014623008
- BERTScore (Question Generation): 0.8813527884907747
- MoverScore (Question Generation): 0.5564529629929519
- Metrics:
- Dataset: lmqg/qg_subjqa (electronics)
- Metrics:
- BLEU4 (Question Generation): 1.1509235130252845e - 06
- ROUGE - L (Question Generation): 0.1347921519214348
- METEOR (Question Generation): 0.1652654590718401
- BERTScore (Question Generation): 0.8771152388648826
- MoverScore (Question Generation): 0.5576801864538657
- Metrics:
- Dataset: lmqg/qg_subjqa (grocery)
- Metrics:
- BLEU4 (Question Generation): 9.978299614007137e - 11
- ROUGE - L (Question Generation): 0.10263878605233773
- METEOR (Question Generation): 0.16240054544628837
- BERTScore (Question Generation): 0.8745810793240865
- MoverScore (Question Generation): 0.5658686637551452
- Metrics:
- Dataset: lmqg/qg_subjqa (movies)
- Metrics:
- BLEU4 (Question Generation): 0.007215098899309626
- ROUGE - L (Question Generation): 0.118923829807047
- METEOR (Question Generation): 0.13060353590956533
- BERTScore (Question Generation): 0.8766350997732831
- MoverScore (Question Generation): 0.5545418638672879
- Metrics:
- Dataset: lmqg/qg_subjqa (restaurants)
- Metrics:
- BLEU4 (Question Generation): 1.7093216558055103e - 10
- ROUGE - L (Question Generation): 0.10704045187993966
- METEOR (Question Generation): 0.13299758428004418
- BERTScore (Question Generation): 0.8783149416832363
- MoverScore (Question Generation): 0.5544508204843501
- Metrics:
- Dataset: lmqg/qg_subjqa (tripadvisor)
- Metrics:
- BLEU4 (Question Generation): 0.009344978745987451
- ROUGE - L (Question Generation): 0.13512247796303523
- METEOR (Question Generation): 0.16514085804298576
- BERTScore (Question Generation): 0.8923153428327643
- MoverScore (Question Generation): 0.5667192018951045
- Metrics:
๐ License
The model is licensed under cc - by - 4.0.
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