--- license: cc-by-4.0 metrics: - bleu4 - meteor - rouge-l - bertscore - moverscore language: en datasets: - lmqg/qg_dequad pipeline_tag: text2text-generation tags: - question generation - answer extraction 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" - text: " Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress." example_title: "Answer Extraction Example 1" - text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress. " example_title: "Answer Extraction Example 2" model-index: - name: lmqg/mt5-small-dequad-multitask results: - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_dequad type: default args: default metrics: - name: BLEU4 type: bleu4 value: 0.008153318257935705 - name: ROUGE-L type: rouge-l value: 0.10153326763371277 - name: METEOR type: meteor value: 0.12181097136639749 - name: BERTScore type: bertscore value: 0.8038890473051649 - name: MoverScore type: moverscore value: 0.551016955735025 --- # Model Card of `lmqg/mt5-small-dequad-multitask` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). This model is fine-tuned on the answer extraction task as well as the question generation. Please cite our paper if you use the model ([https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)). ``` @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", } ``` ### Overview - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small) - **Language:** en - **Training data:** [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) (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-dequad-multitask') # model prediction question_answer = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline # initialize model pipe = pipeline("text2text-generation", 'lmqg/mt5-small-dequad-multitask') # answer extraction answer = pipe('extract answers: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.') # question generation question = pipe('generate question: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.') ``` ## Evaluation Metrics ### Metrics | Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link | |:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:| | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) | default | 0.008 | 0.102 | 0.122 | 0.804 | 0.551 | [link](https://huggingface.co/lmqg/mt5-small-dequad-multitask/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_dequad.default.json) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_dequad - dataset_name: default - input_types: ['paragraph_answer', 'paragraph_sentence'] - output_types: ['question', 'answer'] - prefix_types: ['qg', 'ae'] - model: google/mt5-small - max_length: 512 - max_length_output: 32 - epoch: 15 - batch: 16 - lr: 0.001 - 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/mt5-small-dequad-multitask/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", } ```