
## T5 for multi-task QA and QG
This is multi-task [t5-base](https://arxiv.org/abs/1910.10683) model trained for question answering and answer aware question generation tasks. 

For question generation the answer spans are highlighted within the text with special highlight tokens (`<hl>`) and prefixed with 'generate question: '. For QA the input is processed like this `question: question_text context: context_text </s>` 

You can play with the model using the inference API. Here's how you can use it

For QG

`generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>`

For QA

`question: What is 42 context: 42 is the answer to life, the universe and everything. </s>`

For more deatils see [this](https://github.com/patil-suraj/question_generation) repo.

{"license": "mit", "tags": ["question-answer-generation"], "datasets": ["squad"], "widget": [{"text": "generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>"}, {"text": "question: What is 42 context: 42 is the answer to life, the universe and everything. </s>"}]}
transformers pytorch t5 text2text-generation question-answer-generation dataset:squad arxiv:1910.10683 license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #t5 #text2text-generation #question-answer-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


## T5 for multi-task QA and QG
This is multi-task t5-base model trained for question answering and answer aware question generation tasks. 

For question generation the answer spans are highlighted within the text with special highlight tokens ('<hl>') and prefixed with 'generate question: '. For QA the input is processed like this 'question: question_text context: context_text </s>' 

You can play with the model using the inference API. Here's how you can use it

For QG

'generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>'

For QA

'question: What is 42 context: 42 is the answer to life, the universe and everything. </s>'

For more deatils see this repo.

## T5 for multi-task QA and QG
This is multi-task t5-base model trained for question answering and answer aware question generation tasks. 

For question generation the answer spans are highlighted within the text with special highlight tokens ('<hl>') and prefixed with 'generate question: '. For QA the input is processed like this 'question: question_text context: context_text </s>' 

You can play with the model using the inference API. Here's how you can use it

For QG

'generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>'

For QA

'question: What is 42 context: 42 is the answer to life, the universe and everything. </s>'

For more deatils see this repo.
TAGS
#transformers #pytorch #t5 #text2text-generation #question-answer-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ## T5 for multi-task QA and QG
This is multi-task t5-base model trained for question answering and answer aware question generation tasks. 

For question generation the answer spans are highlighted within the text with special highlight tokens ('<hl>') and prefixed with 'generate question: '. For QA the input is processed like this 'question: question_text context: context_text </s>' 

You can play with the model using the inference API. Here's how you can use it

For QG

'generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>'

For QA

'question: What is 42 context: 42 is the answer to life, the universe and everything. </s>'

For more deatils see this repo.
passage: TAGS
#transformers #pytorch #t5 #text2text-generation #question-answer-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
## T5 for multi-task QA and QG
This is multi-task t5-base model trained for question answering and answer aware question generation tasks. 

For question generation the answer spans are highlighted within the text with special highlight tokens ('<hl>') and prefixed with 'generate question: '. For QA the input is processed like this 'question: question_text context: context_text </s>' 

You can play with the model using the inference API. Here's how you can use it

For QG

'generate question: <hl> 42 <hl> is the answer to life, the universe and everything. </s>'

For QA

'question: What is 42 context: 42 is the answer to life, the universe and everything. </s>'

For more deatils see this repo.