--- license: cc-by-4.0 metrics: - bleu4 - meteor - rouge-l - bertscore - moverscore language: en datasets: - lmqg/qag_tweetqa pipeline_tag: text2text-generation tags: - questions and answers generation widget: - text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records." example_title: "Questions & Answers Generation Example 1" model-index: - name: research-backup/t5-base-tweetqa-qag-np results: - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qag_tweetqa type: default args: default metrics: - name: BLEU4 (Question & Answer Generation) type: bleu4_question_answer_generation value: 13.4 - name: ROUGE-L (Question & Answer Generation) type: rouge_l_question_answer_generation value: 37.23 - name: METEOR (Question & Answer Generation) type: meteor_question_answer_generation value: 31.14 - name: BERTScore (Question & Answer Generation) type: bertscore_question_answer_generation value: 90.8 - name: MoverScore (Question & Answer Generation) type: moverscore_question_answer_generation value: 62.26 - name: QAAlignedF1Score-BERTScore (Question & Answer Generation) type: qa_aligned_f1_score_bertscore_question_answer_generation value: 92.4 - name: QAAlignedRecall-BERTScore (Question & Answer Generation) type: qa_aligned_recall_bertscore_question_answer_generation value: 92.03 - name: QAAlignedPrecision-BERTScore (Question & Answer Generation) type: qa_aligned_precision_bertscore_question_answer_generation value: 92.78 - name: QAAlignedF1Score-MoverScore (Question & Answer Generation) type: qa_aligned_f1_score_moverscore_question_answer_generation value: 64.83 - name: QAAlignedRecall-MoverScore (Question & Answer Generation) type: qa_aligned_recall_moverscore_question_answer_generation value: 64.07 - name: QAAlignedPrecision-MoverScore (Question & Answer Generation) type: qa_aligned_precision_moverscore_question_answer_generation value: 65.68 --- # Model Card of `research-backup/t5-base-tweetqa-qag-np` This model is fine-tuned version of [t5-base](https://huggingface.co/t5-base) for question & answer pair generation task on the [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). This model is fine-tuned without a task prefix. ### Overview - **Language model:** [t5-base](https://huggingface.co/t5-base) - **Language:** en - **Training data:** [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) (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="research-backup/t5-base-tweetqa-qag-np") # model prediction question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "research-backup/t5-base-tweetqa-qag-np") output = pipe("Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.") ``` ## Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/research-backup/t5-base-tweetqa-qag-np/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_tweetqa.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:---------------------------------------------------------------------| | BERTScore | 90.8 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | Bleu_1 | 40.49 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | Bleu_2 | 27.77 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | Bleu_3 | 19.18 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | Bleu_4 | 13.4 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | METEOR | 31.14 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | MoverScore | 62.26 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | QAAlignedF1Score (BERTScore) | 92.4 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | QAAlignedF1Score (MoverScore) | 64.83 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | QAAlignedPrecision (BERTScore) | 92.78 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | QAAlignedPrecision (MoverScore) | 65.68 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | QAAlignedRecall (BERTScore) | 92.03 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | QAAlignedRecall (MoverScore) | 64.07 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | | ROUGE_L | 37.23 | default | [lmqg/qag_tweetqa](https://huggingface.co/datasets/lmqg/qag_tweetqa) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_tweetqa - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: None - model: t5-base - max_length: 256 - max_length_output: 128 - epoch: 15 - batch: 32 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 2 - label_smoothing: 0.0 The full configuration can be found at [fine-tuning config file](https://huggingface.co/research-backup/t5-base-tweetqa-qag-np/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", } ```