# unifiedqg-bart-base

## Model description

This model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output.   
It is based on a pretrained `bart-base` model.     

#### How to use

The model takes concatenated context and answers as an input sequence, and will generate a full distractor sentence as an output sequence. The max sequence length is 1024 tokens. Inputs should be organised into the following format:   
```
answer \n context 
```
The input sequence can then be encoded and passed as the `input_ids` argument in the model's `generate()` method.


{"language": "en", "tags": ["bart", "question", "generation", "seq2seq"], "datasets": ["unifiedQA"], "metrics": ["bleu", "rouge"], "pipeline_tag": "text2text-generation", "widget": [{"text": "treehouses in france. \n 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 ."}]}
transformers pytorch safetensors bart text2text-generation question generation seq2seq en dataset:unifiedQA autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #question #generation #seq2seq #en #dataset-unifiedQA #autotrain_compatible #endpoints_compatible #region-us 

# unifiedqg-bart-base

## Model description

This model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output.   
It is based on a pretrained 'bart-base' model.     

#### How to use

The model takes concatenated context and answers as an input sequence, and will generate a full distractor sentence as an output sequence. The max sequence length is 1024 tokens. Inputs should be organised into the following format:   

The input sequence can then be encoded and passed as the 'input_ids' argument in the model's 'generate()' method.


# unifiedqg-bart-base ## Model description

This model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output.   
It is based on a pretrained 'bart-base' model. #### How to use

The model takes concatenated context and answers as an input sequence, and will generate a full distractor sentence as an output sequence. The max sequence length is 1024 tokens. Inputs should be organised into the following format:   

The input sequence can then be encoded and passed as the 'input_ids' argument in the model's 'generate()' method.
TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #question #generation #seq2seq #en #dataset-unifiedQA #autotrain_compatible #endpoints_compatible #region-us 
 # unifiedqg-bart-base ## Model description

This model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output.   
It is based on a pretrained 'bart-base' model. #### How to use

The model takes concatenated context and answers as an input sequence, and will generate a full distractor sentence as an output sequence. The max sequence length is 1024 tokens. Inputs should be organised into the following format:   

The input sequence can then be encoded and passed as the 'input_ids' argument in the model's 'generate()' method.
passage: TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #question #generation #seq2seq #en #dataset-unifiedQA #autotrain_compatible #endpoints_compatible #region-us 
# unifiedqg-bart-base## Model description

This model is a sequence-to-sequence question generator which takes an answer and context as an input, and generates a question as an output.   
It is based on a pretrained 'bart-base' model.#### How to use

The model takes concatenated context and answers as an input sequence, and will generate a full distractor sentence as an output sequence. The max sequence length is 1024 tokens. Inputs should be organised into the following format:   

The input sequence can then be encoded and passed as the 'input_ids' argument in the model's 'generate()' method.