--- 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: "generate question: 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: "generate question: 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: "generate question: 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 results: - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_squad type: default args: default metrics: - name: BLEU4 type: bleu4 value: 0.26168385362299557 - name: ROUGE-L type: rouge-l value: 0.5384959163821219 - name: METEOR type: meteor value: 0.27073122286541956 - name: BERTScore type: bertscore value: 0.9100413219045603 - name: MoverScore type: moverscore value: 0.6499011626820898 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_squadshifts type: reddit args: reddit metrics: - name: BLEU4 type: bleu4 value: 0.059525104157825456 - name: ROUGE-L type: rouge-l value: 0.22365090580055863 - name: METEOR type: meteor value: 0.21499800504546457 - name: BERTScore type: bertscore value: 0.9095144685254328 - name: MoverScore type: moverscore value: 0.6059332247878408 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_squadshifts type: new_wiki args: new_wiki metrics: - name: BLEU4 type: bleu4 value: 0.11118273173452982 - name: ROUGE-L type: rouge-l value: 0.2967546690273089 - name: METEOR type: meteor value: 0.27315087810722966 - name: BERTScore type: bertscore value: 0.9322739617807421 - name: MoverScore type: moverscore value: 0.6623000084761579 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_subjqa type: tripadvisor args: tripadvisor metrics: - name: BLEU4 type: bleu4 value: 8.380171318718442e-07 - name: ROUGE-L type: rouge-l value: 0.1402922852924756 - name: METEOR type: meteor value: 0.1372146070365174 - name: BERTScore type: bertscore value: 0.8891002409937424 - name: MoverScore type: moverscore value: 0.5604572211470809 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_squadshifts type: default args: default metrics: - name: BLEU4 type: bleu4 value: 0.07839941048417529 - name: ROUGE-L type: rouge-l value: 0.25357667226247294 - name: METEOR type: meteor value: 0.24046838149047955 - name: BERTScore type: bertscore value: 0.9182198703598111 - name: MoverScore type: moverscore value: 0.6274693859765924 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_squadshifts type: nyt args: nyt metrics: - name: BLEU4 type: bleu4 value: 0.08117757543966063 - name: ROUGE-L type: rouge-l value: 0.25292097720734297 - name: METEOR type: meteor value: 0.25254205113198686 - name: BERTScore type: bertscore value: 0.9249009759439454 - name: MoverScore type: moverscore value: 0.6406329128556304 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_subjqa type: restaurants args: restaurants metrics: - name: BLEU4 type: bleu4 value: 1.1301750984972448e-06 - name: ROUGE-L type: rouge-l value: 0.13083168975354642 - name: METEOR type: meteor value: 0.12419733006916912 - name: BERTScore type: bertscore value: 0.8797711839570719 - name: MoverScore type: moverscore value: 0.5542757411268555 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_subjqa type: electronics args: electronics metrics: - name: BLEU4 type: bleu4 value: 0.00866799444965211 - name: ROUGE-L type: rouge-l value: 0.1601628874804186 - name: METEOR type: meteor value: 0.15348605312210778 - name: BERTScore type: bertscore value: 0.8783386920680519 - name: MoverScore type: moverscore value: 0.5634845371093992 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_subjqa type: books args: books metrics: - name: BLEU4 type: bleu4 value: 0.006278914808207679 - name: ROUGE-L type: rouge-l value: 0.12368226019088967 - name: METEOR type: meteor value: 0.11576293675813865 - name: BERTScore type: bertscore value: 0.8807110440044503 - name: MoverScore type: moverscore value: 0.5555905941686486 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_subjqa type: movies args: movies metrics: - name: BLEU4 type: bleu4 value: 1.0121579426501661e-06 - name: ROUGE-L type: rouge-l value: 0.12508697028506718 - name: METEOR type: meteor value: 0.11862284941640638 - name: BERTScore type: bertscore value: 0.8748829724726739 - name: MoverScore type: moverscore value: 0.5528899173535703 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_subjqa type: grocery args: grocery metrics: - name: BLEU4 type: bleu4 value: 0.00528043272450429 - name: ROUGE-L type: rouge-l value: 0.12343711316491492 - name: METEOR type: meteor value: 0.15133496445452477 - name: BERTScore type: bertscore value: 0.8778951253890991 - name: MoverScore type: moverscore value: 0.5701949938103265 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_squadshifts type: amazon args: amazon metrics: - name: BLEU4 type: bleu4 value: 0.06530369842068952 - name: ROUGE-L type: rouge-l value: 0.25030985091008146 - name: METEOR type: meteor value: 0.2229994442645732 - name: BERTScore type: bertscore value: 0.9092814804525936 - name: MoverScore type: moverscore value: 0.6086538514008419 - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_subjqa type: default args: default metrics: - name: BLEU4 type: bleu4 value: 0.005121882223046874 - name: ROUGE-L type: rouge-l value: 0.1346485324169255 - name: METEOR type: meteor value: 0.13733272662214893 - name: BERTScore type: bertscore value: 0.8811488576438816 - name: MoverScore type: moverscore value: 0.5614233235005509 --- # Language Models Fine-tuning on Question Generation: `lmqg/bart-large-squad` 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). ### 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:** [TBA](TBA) ### Usage ```python from transformers import pipeline model_path = 'lmqg/bart-large-squad' pipe = pipeline("text2text-generation", model_path) # Question Generation input_text = 'generate question: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.' question = pipe(input_text) ``` ## Evaluation Metrics ### Metrics | Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link | |:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:| | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | default | 0.26168385362299557 | 0.5384959163821219 | 0.27073122286541956 | 0.9100413219045603 | 0.6499011626820898 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | ### Out-of-domain Metrics | Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link | |:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:| | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | reddit | 0.059525104157825456 | 0.22365090580055863 | 0.21499800504546457 | 0.9095144685254328 | 0.6059332247878408 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.reddit.json) | | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | new_wiki | 0.11118273173452982 | 0.2967546690273089 | 0.27315087810722966 | 0.9322739617807421 | 0.6623000084761579 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.new_wiki.json) | | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | tripadvisor | 8.380171318718442e-07 | 0.1402922852924756 | 0.1372146070365174 | 0.8891002409937424 | 0.5604572211470809 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.tripadvisor.json) | | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | default | 0.07839941048417529 | 0.25357667226247294 | 0.24046838149047955 | 0.9182198703598111 | 0.6274693859765924 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.default.json) | | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | nyt | 0.08117757543966063 | 0.25292097720734297 | 0.25254205113198686 | 0.9249009759439454 | 0.6406329128556304 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.nyt.json) | | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | restaurants | 1.1301750984972448e-06 | 0.13083168975354642 | 0.12419733006916912 | 0.8797711839570719 | 0.5542757411268555 | [link](https://huggingface.co/lmqg/bart-large-squad/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) | electronics | 0.00866799444965211 | 0.1601628874804186 | 0.15348605312210778 | 0.8783386920680519 | 0.5634845371093992 | [link](https://huggingface.co/lmqg/bart-large-squad/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) | books | 0.006278914808207679 | 0.12368226019088967 | 0.11576293675813865 | 0.8807110440044503 | 0.5555905941686486 | [link](https://huggingface.co/lmqg/bart-large-squad/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) | movies | 1.0121579426501661e-06 | 0.12508697028506718 | 0.11862284941640638 | 0.8748829724726739 | 0.5528899173535703 | [link](https://huggingface.co/lmqg/bart-large-squad/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) | grocery | 0.00528043272450429 | 0.12343711316491492 | 0.15133496445452477 | 0.8778951253890991 | 0.5701949938103265 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.grocery.json) | | [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | amazon | 0.06530369842068952 | 0.25030985091008146 | 0.2229994442645732 | 0.9092814804525936 | 0.6086538514008419 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.amazon.json) | | [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | default | 0.005121882223046874 | 0.1346485324169255 | 0.13733272662214893 | 0.8811488576438816 | 0.5614233235005509 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.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: 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/raw/main/trainer_config.json). ## Citation TBA