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--- |
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license: cc-by-4.0 |
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metrics: |
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- bleu4 |
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- meteor |
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- rouge-l |
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- bertscore |
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- moverscore |
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language: en |
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datasets: |
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- lmqg/qg_dequad |
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pipeline_tag: text2text-generation |
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tags: |
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- question generation |
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widget: |
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- text: "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records." |
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example_title: "Question Generation Example 1" |
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- text: "Beyonce further expanded her acting career, starring as blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac Records." |
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example_title: "Question Generation Example 2" |
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- text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, <hl> Cadillac Records <hl> ." |
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example_title: "Question Generation Example 3" |
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model-index: |
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- name: lmqg/mbart-large-cc25-dequad |
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results: |
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- task: |
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name: Text2text Generation |
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type: text2text-generation |
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dataset: |
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name: lmqg/qg_dequad |
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type: default |
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args: default |
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metrics: |
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- name: BLEU4 |
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type: bleu4 |
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value: 0.007531690677542128 |
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- name: ROUGE-L |
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type: rouge-l |
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value: 0.1118739967466843 |
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- name: METEOR |
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type: meteor |
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value: 0.1371220015206642 |
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- name: BERTScore |
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type: bertscore |
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value: 0.8076776969325791 |
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- name: MoverScore |
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type: moverscore |
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value: 0.5588358947114936 |
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--- |
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# Model Card of `lmqg/mbart-large-cc25-dequad` |
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This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) for question generation task on the |
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[lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). |
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Please cite our paper if you use the model ([https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)). |
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``` |
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@inproceedings{ushio-etal-2022-generative, |
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title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", |
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author = "Ushio, Asahi and |
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Alva-Manchego, Fernando and |
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Camacho-Collados, Jose", |
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booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", |
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month = dec, |
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year = "2022", |
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address = "Abu Dhabi, U.A.E.", |
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publisher = "Association for Computational Linguistics", |
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} |
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``` |
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### Overview |
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- **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) |
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- **Language:** en |
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- **Training data:** [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (default) |
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- **Online Demo:** [https://autoqg.net/](https://autoqg.net/) |
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- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) |
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- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) |
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### Usage |
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- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) |
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```python |
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from lmqg import TransformersQG |
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# initialize model |
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model = TransformersQG(language='en', model='lmqg/mbart-large-cc25-dequad') |
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# model prediction |
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question = model.generate_q(list_context=["William Turner was an English painter who specialised in watercolour landscapes"], list_answer=["William Turner"]) |
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``` |
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- With `transformers` |
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```python |
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from transformers import pipeline |
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# initialize model |
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pipe = pipeline("text2text-generation", 'lmqg/mbart-large-cc25-dequad') |
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# question generation |
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question = pipe('<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.') |
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``` |
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## Evaluation Metrics |
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### Metrics |
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| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link | |
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|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:| |
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| [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | default | 0.008 | 0.112 | 0.137 | 0.808 | 0.559 | [link](https://huggingface.co/lmqg/mbart-large-cc25-dequad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_dequad.default.json) | |
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## Training hyperparameters |
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The following hyperparameters were used during fine-tuning: |
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- dataset_path: lmqg/qg_dequad |
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- dataset_name: default |
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- input_types: ['paragraph_answer'] |
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- output_types: ['question'] |
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- prefix_types: None |
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- model: facebook/mbart-large-cc25 |
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- max_length: 512 |
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- max_length_output: 32 |
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- epoch: 11 |
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- batch: 4 |
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- lr: 0.0001 |
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- fp16: False |
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- random_seed: 1 |
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- gradient_accumulation_steps: 16 |
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- label_smoothing: 0.15 |
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The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-dequad/raw/main/trainer_config.json). |
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## Citation |
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``` |
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@inproceedings{ushio-etal-2022-generative, |
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title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", |
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author = "Ushio, Asahi and |
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Alva-Manchego, Fernando and |
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Camacho-Collados, Jose", |
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booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", |
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month = dec, |
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year = "2022", |
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address = "Abu Dhabi, U.A.E.", |
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publisher = "Association for Computational Linguistics", |
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} |
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``` |
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