--- 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 widget: - text: " Beyonce 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: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records." example_title: "Question Generation Example 2" - text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records ." example_title: "Question Generation Example 3" model-index: - name: lmqg/bart-large-squad-qg 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: 26.17 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 53.85 - name: METEOR (Question Generation) type: meteor_question_generation value: 27.07 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 91.0 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 64.99 - 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: 95.54 - 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: 95.49 - 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: 95.59 - 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: 70.82 - 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: 70.54 - 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: 71.13 - name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer value: 93.23 - name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer value: 93.35 - name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer value: 93.13 - name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer value: 64.76 - name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer value: 64.63 - name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer value: 64.98 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_squadshifts type: amazon args: amazon metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 0.06530369842068952 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.25030985091008146 - name: METEOR (Question Generation) type: meteor_question_generation value: 0.2229994442645732 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.9092814804525936 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.6086538514008419 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_squadshifts type: new_wiki args: new_wiki metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 0.11118273173452982 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.2967546690273089 - name: METEOR (Question Generation) type: meteor_question_generation value: 0.27315087810722966 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.9322739617807421 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.6623000084761579 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_squadshifts type: nyt args: nyt metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 0.08117757543966063 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.25292097720734297 - name: METEOR (Question Generation) type: meteor_question_generation value: 0.25254205113198686 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.9249009759439454 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.6406329128556304 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_squadshifts type: reddit args: reddit metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 0.059525104157825456 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.22365090580055863 - name: METEOR (Question Generation) type: meteor_question_generation value: 0.21499800504546457 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.9095144685254328 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.6059332247878408 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_subjqa type: books args: books metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 0.006278914808207679 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.12368226019088967 - name: METEOR (Question Generation) type: meteor_question_generation value: 0.11576293675813865 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.8807110440044503 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.5555905941686486 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_subjqa type: electronics args: electronics metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 0.00866799444965211 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.1601628874804186 - name: METEOR (Question Generation) type: meteor_question_generation value: 0.15348605312210778 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.8783386920680519 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.5634845371093992 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_subjqa type: grocery args: grocery metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 0.00528043272450429 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.12343711316491492 - name: METEOR (Question Generation) type: meteor_question_generation value: 0.15133496445452477 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.8778951253890991 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.5701949938103265 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_subjqa type: movies args: movies metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 1.0121579426501661e-06 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.12508697028506718 - name: METEOR (Question Generation) type: meteor_question_generation value: 0.11862284941640638 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.8748829724726739 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.5528899173535703 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_subjqa type: restaurants args: restaurants metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 1.1301750984972448e-06 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.13083168975354642 - name: METEOR (Question Generation) type: meteor_question_generation value: 0.12419733006916912 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.8797711839570719 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.5542757411268555 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_subjqa type: tripadvisor args: tripadvisor metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 8.380171318718442e-07 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.1402922852924756 - name: METEOR (Question Generation) type: meteor_question_generation value: 0.1372146070365174 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.8891002409937424 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.5604572211470809 --- # Model Card of `lmqg/bart-large-squad-qg` This model is fine-tuned version of [facebook/bart-large](https://huggingface.co./facebook/bart-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [facebook/bart-large](https://huggingface.co./facebook/bart-large) - **Language:** en - **Training data:** [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) (default) - **Online Demo:** [https://autoqg.net/](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](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="en", model="lmqg/bart-large-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` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/bart-large-squad-qg") output = pipe(" Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co./lmqg/bart-large-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:---------------------------------------------------------------| | BERTScore | 91 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | Bleu_1 | 58.79 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | Bleu_2 | 42.79 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | Bleu_3 | 33.11 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | Bleu_4 | 26.17 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | METEOR | 27.07 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | MoverScore | 64.99 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | ROUGE_L | 53.85 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | - ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co./lmqg/bart-large-squad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:---------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 95.54 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | QAAlignedF1Score (MoverScore) | 70.82 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | QAAlignedPrecision (BERTScore) | 95.59 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | QAAlignedPrecision (MoverScore) | 71.13 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | QAAlignedRecall (BERTScore) | 95.49 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | QAAlignedRecall (MoverScore) | 70.54 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | - ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/bart-large-squad-ae`](https://huggingface.co./lmqg/bart-large-squad-ae). [raw metric file](https://huggingface.co./lmqg/bart-large-squad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.lmqg_bart-large-squad-ae.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:---------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 93.23 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | QAAlignedF1Score (MoverScore) | 64.76 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | QAAlignedPrecision (BERTScore) | 93.13 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | QAAlignedPrecision (MoverScore) | 64.98 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | QAAlignedRecall (BERTScore) | 93.35 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | | QAAlignedRecall (MoverScore) | 64.63 | default | [lmqg/qg_squad](https://huggingface.co./datasets/lmqg/qg_squad) | - ***Metrics (Question Generation, Out-of-Domain)*** | Dataset | Type | BERTScore| Bleu_4 | METEOR | MoverScore | ROUGE_L | Link | |:--------|:-----|---------:|-------:|-------:|-----------:|--------:|-----:| | [lmqg/qg_squadshifts](https://huggingface.co./datasets/lmqg/qg_squadshifts) | amazon | 90.93 | 6.53 | 22.3 | 60.87 | 25.03 | [link](https://huggingface.co./lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.amazon.json) | | [lmqg/qg_squadshifts](https://huggingface.co./datasets/lmqg/qg_squadshifts) | new_wiki | 93.23 | 11.12 | 27.32 | 66.23 | 29.68 | [link](https://huggingface.co./lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.new_wiki.json) | | [lmqg/qg_squadshifts](https://huggingface.co./datasets/lmqg/qg_squadshifts) | nyt | 92.49 | 8.12 | 25.25 | 64.06 | 25.29 | [link](https://huggingface.co./lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.nyt.json) | | [lmqg/qg_squadshifts](https://huggingface.co./datasets/lmqg/qg_squadshifts) | reddit | 90.95 | 5.95 | 21.5 | 60.59 | 22.37 | [link](https://huggingface.co./lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.reddit.json) | | [lmqg/qg_subjqa](https://huggingface.co./datasets/lmqg/qg_subjqa) | books | 88.07 | 0.63 | 11.58 | 55.56 | 12.37 | [link](https://huggingface.co./lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.books.json) | | [lmqg/qg_subjqa](https://huggingface.co./datasets/lmqg/qg_subjqa) | electronics | 87.83 | 0.87 | 15.35 | 56.35 | 16.02 | [link](https://huggingface.co./lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.electronics.json) | | [lmqg/qg_subjqa](https://huggingface.co./datasets/lmqg/qg_subjqa) | grocery | 87.79 | 0.53 | 15.13 | 57.02 | 12.34 | [link](https://huggingface.co./lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.grocery.json) | | [lmqg/qg_subjqa](https://huggingface.co./datasets/lmqg/qg_subjqa) | movies | 87.49 | 0.0 | 11.86 | 55.29 | 12.51 | [link](https://huggingface.co./lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.movies.json) | | [lmqg/qg_subjqa](https://huggingface.co./datasets/lmqg/qg_subjqa) | restaurants | 87.98 | 0.0 | 12.42 | 55.43 | 13.08 | [link](https://huggingface.co./lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.restaurants.json) | | [lmqg/qg_subjqa](https://huggingface.co./datasets/lmqg/qg_subjqa) | tripadvisor | 88.91 | 0.0 | 13.72 | 56.05 | 14.03 | [link](https://huggingface.co./lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.tripadvisor.json) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_squad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: facebook/bart-large - max_length: 512 - max_length_output: 32 - epoch: 4 - batch: 32 - lr: 5e-05 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co./lmqg/bart-large-squad-qg/raw/main/trainer_config.json). ## 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", } ```