--- license: cc-by-4.0 metrics: - bleu4 - meteor - rouge-l - bertscore - moverscore language: es datasets: - lmqg/qag_esquad pipeline_tag: text2text-generation tags: - questions and answers generation widget: - text: "del Ministerio de Desarrollo Urbano , Gobierno de la India." example_title: "Questions & Answers Generation Example 1" model-index: - name: lmqg/mt5-base-esquad-qag results: - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qag_esquad type: default args: default metrics: - name: BLEU4 (Question & Answer Generation) type: bleu4_question_answer_generation value: 1.53 - name: ROUGE-L (Question & Answer Generation) type: rouge_l_question_answer_generation value: 13.3 - name: METEOR (Question & Answer Generation) type: meteor_question_answer_generation value: 17.56 - name: BERTScore (Question & Answer Generation) type: bertscore_question_answer_generation value: 64.69 - name: MoverScore (Question & Answer Generation) type: moverscore_question_answer_generation value: 50.34 - name: QAAlignedF1Score-BERTScore (Question & Answer Generation) type: qa_aligned_f1_score_bertscore_question_answer_generation value: 78.96 - name: QAAlignedRecall-BERTScore (Question & Answer Generation) type: qa_aligned_recall_bertscore_question_answer_generation value: 79.31 - name: QAAlignedPrecision-BERTScore (Question & Answer Generation) type: qa_aligned_precision_bertscore_question_answer_generation value: 78.66 - name: QAAlignedF1Score-MoverScore (Question & Answer Generation) type: qa_aligned_f1_score_moverscore_question_answer_generation value: 54.3 - name: QAAlignedRecall-MoverScore (Question & Answer Generation) type: qa_aligned_recall_moverscore_question_answer_generation value: 54.42 - name: QAAlignedPrecision-MoverScore (Question & Answer Generation) type: qa_aligned_precision_moverscore_question_answer_generation value: 54.21 --- # Model Card of `lmqg/mt5-base-esquad-qag` This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question & answer pair generation task on the [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base) - **Language:** es - **Training data:** [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) (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="es", model="lmqg/mt5-base-esquad-qag") # model prediction question_answer_pairs = model.generate_qa("a noviembre , que es también la estación lluviosa.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-base-esquad-qag") output = pipe("del Ministerio de Desarrollo Urbano , Gobierno de la India.") ``` ## Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-esquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_esquad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-------------------------------------------------------------------| | BERTScore | 64.69 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) | | Bleu_1 | 7.9 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) | | Bleu_2 | 3.81 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) | | Bleu_3 | 2.27 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) | | Bleu_4 | 1.53 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) | | METEOR | 17.56 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) | | MoverScore | 50.34 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) | | QAAlignedF1Score (BERTScore) | 78.96 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) | | QAAlignedF1Score (MoverScore) | 54.3 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) | | QAAlignedPrecision (BERTScore) | 78.66 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) | | QAAlignedPrecision (MoverScore) | 54.21 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) | | QAAlignedRecall (BERTScore) | 79.31 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) | | QAAlignedRecall (MoverScore) | 54.42 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) | | ROUGE_L | 13.3 | default | [lmqg/qag_esquad](https://huggingface.co/datasets/lmqg/qag_esquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_esquad - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: None - model: google/mt5-base - max_length: 512 - max_length_output: 256 - epoch: 13 - batch: 2 - lr: 0.0005 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 32 - label_smoothing: 0.0 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-esquad-qag/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", } ```