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EN
Bart Base Squad Qg
This is a question generation model fine-tuned on the BART-base architecture, specifically designed to generate relevant questions from given text and answers.
Downloads 57
Release Time : 3/2/2022
Model Overview
This model is fine-tuned on facebook/bart-base for question generation tasks on the SQuAD dataset, capable of generating relevant questions based on highlighted answers in the text.
Model Features
High-Quality Question Generation
Fine-tuned on the SQuAD dataset, capable of generating questions highly relevant to the context and answers.
Multi-Metric Evaluation
Provides evaluation results for multiple metrics including BLEU, METEOR, and ROUGE-L.
Out-of-Domain Adaptability
Evaluated on multiple out-of-domain datasets, demonstrating strong generalization capabilities.
Model Capabilities
Text Generation
Question Generation
Natural Language Processing
Use Cases
Education
Automated Reading Comprehension Question Generation
Automatically generates reading comprehension questions based on textbook content.
Generated questions are highly relevant to the original text.
Content Creation
Article-Specific Question Generation
Generates discussion questions for news articles.
Helps readers better understand and reflect on the article content.
đ lmqg/bart-base-squad-qg Model Card
This model is a fine - tuned version of facebook/bart-base for the question generation task on the lmqg/qg_squad (dataset_name: default) via lmqg. It aims to generate high - quality questions from given text, which is useful in various natural language processing applications.
đ Quick Start
Overview
| Property | Details |
|---|---|
| Language model | facebook/bart-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 |
đģ Usage Examples
Basic Usage
# With `lmqg`
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="en", model="lmqg/bart-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")
Advanced Usage
# With `transformers`
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/bart-base-squad-qg")
output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
đ Documentation
Metrics
The model is evaluated using the following metrics:
- bleu4
- meteor
- rouge - l
- bertscore
- moverscore
Widget Examples
- Question Generation Example 1:
- Text: "
Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
- Text: "
- Question Generation Example 2:
- Text: "Beyonce further expanded her acting career, starring as blues singer
Etta James in the 2008 musical biopic, Cadillac Records."
- Text: "Beyonce further expanded her acting career, starring as blues singer
- Question Generation Example 3:
- Text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic,
Cadillac Records ."
- Text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic,
Model Index
The model lmqg/bart-base-squad-qg has the following evaluation results:
On lmqg/qg_squad
| Metric Name | Type | Value |
|---|---|---|
| BLEU4 (Question Generation) | bleu4_question_generation | 24.68 |
| ROUGE - L (Question Generation) | rouge_l_question_generation | 52.66 |
| METEOR (Question Generation) | meteor_question_generation | 26.05 |
| BERTScore (Question Generation) | bertscore_question_generation | 90.87 |
| MoverScore (Question Generation) | moverscore_question_generation | 64.47 |
| QAAlignedF1Score - BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] | qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer | 95.49 |
| QAAlignedRecall - BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] | qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer | 95.44 |
| QAAlignedPrecision - BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] | qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer | 95.55 |
| QAAlignedF1Score - MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] | qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer | 70.38 |
| QAAlignedRecall - MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] | qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer | 70.1 |
| QAAlignedPrecision - MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] | qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_gold_answer | 70.67 |
| QAAlignedF1Score - BERTScore (Question & Answer Generation) [Gold Answer] | qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer | 92.84 |
| QAAlignedRecall - BERTScore (Question & Answer Generation) [Gold Answer] | qa_aligned_recall_bertscore_question_answer_generation_gold_answer | 92.95 |
| QAAlignedPrecision - BERTScore (Question & Answer Generation) [Gold Answer] | qa_aligned_precision_bertscore_question_answer_generation_gold_answer | 92.75 |
| QAAlignedF1Score - MoverScore (Question & Answer Generation) [Gold Answer] | qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer | 64.24 |
| QAAlignedRecall - MoverScore (Question & Answer Generation) [Gold Answer] | qa_aligned_recall_moverscore_question_answer_generation_gold_answer | 64.11 |
| QAAlignedPrecision - MoverScore (Question & Answer Generation) [Gold Answer] | qa_aligned_precision_moverscore_question_answer_generation_gold_answer | 64.46 |
On lmqg/qg_squadshifts (amazon)
| Metric Name | Type | Value |
|---|---|---|
| BLEU4 (Question Generation) | bleu4_question_generation | 0.05824165264328302 |
| ROUGE - L (Question Generation) | rouge_l_question_generation | 0.23816054441894524 |
| METEOR (Question Generation) | meteor_question_generation | 0.2126541577267873 |
| BERTScore (Question Generation) | bertscore_question_generation | 0.9049284884636415 |
| MoverScore (Question Generation) | moverscore_question_generation | 0.6026811246610306 |
On lmqg/qg_squadshifts (new_wiki)
| Metric Name | Type | Value |
|---|---|---|
| BLEU4 (Question Generation) | bleu4_question_generation | 0.10732253983426589 |
| ROUGE - L (Question Generation) | rouge_l_question_generation | 0.2843539251435107 |
| METEOR (Question Generation) | meteor_question_generation | 0.26233713078026283 |
| BERTScore (Question Generation) | bertscore_question_generation | 0.9307303692241476 |
| MoverScore (Question Generation) | moverscore_question_generation | 0.656720781293701 |
On lmqg/qg_squadshifts (nyt)
| Metric Name | Type | Value |
|---|---|---|
| BLEU4 (Question Generation) | bleu4_question_generation | 0.07645313983751752 |
| ROUGE - L (Question Generation) | rouge_l_question_generation | 0.2390325229516282 |
| METEOR (Question Generation) | meteor_question_generation | 0.244330483594333 |
| BERTScore (Question Generation) | bertscore_question_generation | 0.9235989114144583 |
| MoverScore (Question Generation) | moverscore_question_generation | 0.6368628469746445 |
On lmqg/qg_squadshifts (reddit)
| Metric Name | Type | Value |
|---|---|---|
| BLEU4 (Question Generation) | bleu4_question_generation | 0.053789810023704955 |
| ROUGE - L (Question Generation) | rouge_l_question_generation | 0.2141155595451475 |
| METEOR (Question Generation) | meteor_question_generation | 0.20395821936787215 |
| BERTScore (Question Generation) | bertscore_question_generation | 0.905714302466044 |
| MoverScore (Question Generation) | moverscore_question_generation | 0.6013927660089013 |
On lmqg/qg_subjqa (books)
| Metric Name | Type | Value |
|---|---|---|
| BLEU4 (Question Generation) | bleu4_question_generation | 1.4952813458186383e - 10 |
| ROUGE - L (Question Generation) | rouge_l_question_generation | 0.10769136267285535 |
| METEOR (Question Generation) | meteor_question_generation | 0.11520101781020654 |
| BERTScore (Question Generation) | bertscore_question_generation | 0.8774975922095214 |
| MoverScore (Question Generation) | moverscore_question_generation | 0.5520873074919223 |
On lmqg/qg_subjqa (electronics)
| Metric Name | Type | Value |
|---|---|---|
| BLEU4 (Question Generation) | bleu4_question_generation | 1.3766381900873328e - 06 |
| ROUGE - L (Question Generation) | rouge_l_question_generation | 0.14287460464803423 |
| METEOR (Question Generation) | meteor_question_generation | 0.14866637711177003 |
| BERTScore (Question Generation) | bertscore_question_generation | 0.8759880110997111 |
| MoverScore (Question Generation) | moverscore_question_generation | 0.5607199201429516 |
On lmqg/qg_subjqa (grocery)
| Metric Name | Type | Value |
|---|---|---|
| BLEU4 (Question Generation) | bleu4_question_generation | 0.006003840641121225 |
| ROUGE - L (Question Generation) | rouge_l_question_generation | 0.1248840598199836 |
| METEOR (Question Generation) | meteor_question_generation | 0.1553374628831024 |
| BERTScore (Question Generation) | bertscore_question_generation | 0.8737966828346252 |
| MoverScore (Question Generation) | moverscore_question_generation | 0.5662545638649026 |
On lmqg/qg_subjqa (movies)
| Metric Name | Type | Value |
|---|---|---|
| BLEU4 (Question Generation) | bleu4_question_generation | 0.0108258720771249 |
| ROUGE - L (Question Generation) | rouge_l_question_generation | 0.1389815289507374 |
| METEOR (Question Generation) | meteor_question_generation | 0.12855849168399078 |
| BERTScore (Question Generation) | bertscore_question_generation | 0.8773110466344016 |
| MoverScore (Question Generation) | moverscore_question_generation | 0.5555164603510797 |
On lmqg/qg_subjqa (restaurants)
| Metric Name | Type | Value |
|---|---|---|
| BLEU4 (Question Generation) | bleu4_question_generation | 1.7873892359263582e - 10 |
| ROUGE - L (Question Generation) | rouge_l_question_generation | 0.12160976589996819 |
| METEOR (Question Generation) | meteor_question_generation | 0.1146979295288459 |
| BERTScore (Question Generation) | bertscore_question_generation | 0.8771339668070569 |
| MoverScore (Question Generation) | moverscore_question_generation | 0.5490739019998478 |
On lmqg/qg_subjqa (tripadvisor)
| Metric Name | Type | Value |
|---|---|---|
| BLEU4 (Question Generation) | bleu4_question_generation | 0.010174680918435602 |
| ROUGE - L (Question Generation) | rouge_l_question_generation | 0.1341425139885307 |
| METEOR (Question Generation) | meteor_question_generation | 0.1391725168440533 |
| BERTScore (Question Generation) | bertscore_question_generation | 0.8877592491739579 |
| MoverScore (Question Generation) | moverscore_question_generation | 0.5590591813016728 |
đ License
This model is licensed under cc - by - 4.0.
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