model update
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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_subjqa
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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: "generate question: <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: "generate question: 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: "generate question: 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/t5-small-subjqa-vanilla-restaurants
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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_subjqa
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type: restaurants
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args: restaurants
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metrics:
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- name: BLEU4
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type: bleu4
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value: 1.5194344626336288e-14
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- name: ROUGE-L
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type: rouge-l
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value: 0.007328137742159685
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- name: METEOR
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type: meteor
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value: 0.00974946931702154
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- name: BERTScore
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type: bertscore
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value: 0.12266698917921852
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- name: MoverScore
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type: moverscore
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value: 0.4945447139781372
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---
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# Language Models Fine-tuning on Question Generation: `lmqg/t5-small-subjqa-vanilla-restaurants`
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This model is fine-tuned version of [t5-small](https://huggingface.co/t5-small) for question generation task on the
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[lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (dataset_name: restaurants).
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### Overview
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- **Language model:** [t5-small](https://huggingface.co/t5-small)
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- **Language:** en
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- **Training data:** [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) (restaurants)
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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:** [TBA](TBA)
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### Usage
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```python
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from transformers import pipeline
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model_path = 'lmqg/t5-small-subjqa-vanilla-restaurants'
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pipe = pipeline("text2text-generation", model_path)
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# Question Generation
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input_text = 'generate question: <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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question = pipe(input_text)
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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_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | restaurants | 1.5194344626336288e-14 | 0.007328137742159685 | 0.00974946931702154 | 0.12266698917921852 | 0.4945447139781372 | [link](https://huggingface.co/lmqg/t5-small-subjqa-vanilla-restaurants/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.restaurants.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_subjqa
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- dataset_name: restaurants
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- input_types: ['paragraph_answer']
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- output_types: ['question']
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- prefix_types: ['qg']
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- model: t5-small
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- max_length: 512
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- max_length_output: 32
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- epoch: 2
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- batch: 32
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- lr: 5e-05
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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/lmqg/t5-small-subjqa-vanilla-restaurants/raw/main/trainer_config.json).
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## Citation
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TBA
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