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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_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: "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-large-squad-no-paragraph
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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
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type: bleu4
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value: 0.2536046006088414
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- name: ROUGE-L
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type: rouge-l
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value: 0.5253010159135093
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- name: METEOR
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type: meteor
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value: 0.26280710288012865
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- name: BERTScore
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type: bertscore
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value: 0.908802768433947
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- name: MoverScore
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type: moverscore
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value: 0.6443595152739032
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---
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# Language Models Fine-tuning on Question Generation: `lmqg/t5-large-squad-no-paragraph`
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This model is fine-tuned version of [t5-large](https://huggingface.co/t5-large) for question generation task on the
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[lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default).
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This model is fine-tuned without pargraph information but only the sentence that contains the answer.
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### Overview
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- **Language model:** [t5-large](https://huggingface.co/t5-large)
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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:** [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-large-squad-no-paragraph'
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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_squad](https://huggingface.co/datasets/lmqg/qg_squad) | default | 0.2536046006088414 | 0.5253010159135093 | 0.26280710288012865 | 0.908802768433947 | 0.6443595152739032 | [link](https://huggingface.co/lmqg/t5-large-squad-no-paragraph/raw/main/eval/metric.first.sentence.sentence_answer.question.lmqg_qg_squad.default.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_squad
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- dataset_name: default
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- input_types: ['sentence_answer']
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- output_types: ['question']
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- prefix_types: ['qg']
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- model: t5-large
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- max_length: 128
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- max_length_output: 32
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- epoch: 6
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- batch: 16
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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-large-squad-no-paragraph/raw/main/trainer_config.json).
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## Citation
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TBA
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