commit files to HF hub
Browse files- README.md +138 -0
- eval/metric.first.answer.paragraph_answer.question.lmqg_qg_squad.default.json +1 -0
- eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json +1 -0
- eval/samples.test.hyp.paragraph_answer.question.lmqg_qg_squad.default.txt +0 -0
- eval/samples.validation.hyp.paragraph_answer.question.lmqg_qg_squad.default.txt +0 -0
README.md
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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_squad
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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: vocabtrimmer/mt5-small-trimmed-en-enquad-qg
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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_squad
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type: default
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args: default
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metrics:
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- name: BLEU4 (Question Generation)
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type: bleu4_question_generation
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value: 21.84
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- name: ROUGE-L (Question Generation)
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type: rouge_l_question_generation
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value: 49.16
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- name: METEOR (Question Generation)
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type: meteor_question_generation
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value: 23.97
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- name: BERTScore (Question Generation)
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type: bertscore_question_generation
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value: 90.06
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- name: MoverScore (Question Generation)
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type: moverscore_question_generation
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value: 62.83
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---
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# Model Card of `vocabtrimmer/mt5-small-trimmed-en-enquad-qg`
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This model is fine-tuned version of [ckpts/mt5-small-trimmed-en](https://huggingface.co/ckpts/mt5-small-trimmed-en) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
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### Overview
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- **Language model:** [ckpts/mt5-small-trimmed-en](https://huggingface.co/ckpts/mt5-small-trimmed-en)
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- **Language:** en
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- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (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="vocabtrimmer/mt5-small-trimmed-en-enquad-qg")
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# model prediction
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questions = 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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pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-en-enquad-qg")
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output = 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
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- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-en-enquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json)
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| | Score | Type | Dataset |
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|:-----------|--------:|:--------|:---------------------------------------------------------------|
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| BERTScore | 90.06 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
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| Bleu_1 | 54.15 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
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| Bleu_2 | 37.79 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
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| Bleu_3 | 28.32 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
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| Bleu_4 | 21.84 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
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| METEOR | 23.97 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
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| MoverScore | 62.83 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
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| ROUGE_L | 49.16 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
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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_squad
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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: ckpts/mt5-small-trimmed-en
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- max_length: 512
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- max_length_output: 32
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- epoch: 14
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- batch: 16
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- lr: 0.0005
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- fp16: False
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- random_seed: 1
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- gradient_accumulation_steps: 4
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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/vocabtrimmer/mt5-small-trimmed-en-enquad-qg/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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eval/metric.first.answer.paragraph_answer.question.lmqg_qg_squad.default.json
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{"validation": {"Bleu_1": 0.49643914561351915, "Bleu_2": 0.34274790067867905, "Bleu_3": 0.2579770665058814, "Bleu_4": 0.20106966414924154}, "test": {"Bleu_1": 0.4712329414758001, "Bleu_2": 0.31670157262174525, "Bleu_3": 0.23281703690500233, "Bleu_4": 0.17706223404520585}}
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eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json
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{"validation": {"Bleu_1": 0.5487791524927265, "Bleu_2": 0.38877111163039796, "Bleu_3": 0.29775789562465454, "Bleu_4": 0.23516165460585023, "METEOR": 0.25059761748243586, "ROUGE_L": 0.5062196861492448, "BERTScore": 0.9021470886631167, "MoverScore": 0.6412840795628193}, "test": {"Bleu_1": 0.5415444645993998, "Bleu_2": 0.3778827193058188, "Bleu_3": 0.2832450676757577, "Bleu_4": 0.21841503222366088, "METEOR": 0.2397164553054052, "ROUGE_L": 0.49161683678052415, "BERTScore": 0.9005570610424014, "MoverScore": 0.6282781462118372}}
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eval/samples.test.hyp.paragraph_answer.question.lmqg_qg_squad.default.txt
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eval/samples.validation.hyp.paragraph_answer.question.lmqg_qg_squad.default.txt
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