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--- |
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language: en |
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tags: |
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- bart |
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- question |
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- generation |
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- seq2seq |
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datasets: |
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- eqg-race |
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metrics: |
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- bleu |
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- rouge |
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pipeline_tag: text2text-generation |
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widget: |
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- text: "When you ' re having a holiday , one of the main questions to ask is which hotel or apartment to choose . However , when it comes to France , you have another special choice : treehouses . In France , treehouses are offered to travelers as a new choice in many places . The price may be a little higher , but you do have a chance to _ your childhood memories . Alain Laurens , one of France ' s top treehouse designers , said , ' Most of the people might have the experience of building a den when they were young . And they like that feeling of freedom when they are children . ' Its fairy - tale style gives travelers a special feeling . It seems as if they are living as a forest king and enjoying the fresh air in the morning . Another kind of treehouse is the ' star cube ' . It gives travelers the chance of looking at the stars shining in the sky when they are going to sleep . Each ' star cube ' not only offers all the comfortable things that a hotel provides for travelers , but also gives them a chance to look for stars by using a telescope . The glass roof allows you to look at the stars from your bed . " |
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--- |
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# voidful/bart-eqg-question-generator |
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## Model description |
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This model is a sequence-to-sequence question generator with only the context as an input, and generates a question as an output. |
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It is based on a pretrained `bart-base` model, and trained on [EQG-RACE](https://github.com/jemmryx/EQG-RACE) corpus. |
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## Intended uses & limitations |
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The model is trained to generate examinations-style multiple choice question. |
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#### How to use |
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The model takes context as an input sequence, and will generate a question as an output sequence. The max sequence length is 1024 tokens. Inputs should be organised into the following format: |
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``` |
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context |
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``` |
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The input sequence can then be encoded and passed as the `input_ids` argument in the model's `generate()` method. |
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