--- 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/mt5-small-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: 21.65 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 48.95 - name: METEOR (Question Generation) type: meteor_question_generation value: 23.83 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 90.01 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 62.75 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_dequad type: default args: default metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 9.242783121165897e-12 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.01556150764938016 - name: METEOR (Question Generation) type: meteor_question_generation value: 0.04809700451843158 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.7353078946893743 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.5036973829954939 - 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: 0.0059191752064594125 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.05208940592236566 - name: METEOR (Question Generation) type: meteor_question_generation value: 0.06021086135293597 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.7494422899749911 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.5062373132800192 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_frquad type: default args: default metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 0.0171464639522496 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.1583673053928925 - name: METEOR (Question Generation) type: meteor_question_generation value: 0.08244973027319356 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.7291012183458674 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.509610854598101 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_itquad type: default args: default metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 0.005438910607183992 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.05010570221421983 - name: METEOR (Question Generation) type: meteor_question_generation value: 0.05890828426558759 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.7260160158030385 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.5023119088393686 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_jaquad type: default args: default metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 4.4114578660129224e-08 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.06084267343290677 - name: METEOR (Question Generation) type: meteor_question_generation value: 0.005149267426183168 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.6608093198082075 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.46526108687696893 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_koquad type: default args: default metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 1.4750917137316939e-12 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.0006466767450454226 - name: METEOR (Question Generation) type: meteor_question_generation value: 0.007310046912436679 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.6634288882769679 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.4586124640357038 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_ruquad type: default args: default metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 4.229109829516021e-12 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 0.009881091250723615 - name: METEOR (Question Generation) type: meteor_question_generation value: 0.017796529053904556 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 0.7089446693028568 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 0.49098728551715626 --- # Model Card of `lmqg/mt5-small-squad-qg` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) 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:** [google/mt5-small](https://huggingface.co/google/mt5-small) - **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/mt5-small-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/mt5-small-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/mt5-small-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:---------------------------------------------------------------| | BERTScore | 90.01 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_1 | 54.07 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_2 | 37.62 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_3 | 28.18 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | Bleu_4 | 21.65 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | METEOR | 23.83 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | MoverScore | 62.75 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | | ROUGE_L | 48.95 | 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_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | default | 73.53 | 0.0 | 4.81 | 50.37 | 1.56 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_dequad.default.json) | | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | default | 74.94 | 0.59 | 6.02 | 50.62 | 5.21 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) | | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | default | 72.91 | 1.71 | 8.24 | 50.96 | 15.84 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json) | | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | default | 72.6 | 0.54 | 5.89 | 50.23 | 5.01 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json) | | [lmqg/qg_jaquad](https://huggingface.co/datasets/lmqg/qg_jaquad) | default | 66.08 | 0.0 | 0.51 | 46.53 | 6.08 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_jaquad.default.json) | | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | default | 66.34 | 0.0 | 0.73 | 45.86 | 0.06 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json) | | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | default | 70.89 | 0.0 | 1.78 | 49.1 | 0.99 | [link](https://huggingface.co/lmqg/mt5-small-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.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: google/mt5-small - max_length: 512 - max_length_output: 32 - epoch: 15 - batch: 64 - lr: 0.0005 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 1 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-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", } ```