
## T5 for question-generation
This is [t5-small](https://arxiv.org/abs/1910.10683) model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens. 

You can play with the model using the inference API, just highlight the answer spans with `<hl>` tokens and end the text with `</s>`. For example

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

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

### Model in action 🚀

You'll need to clone the [repo](https://github.com/patil-suraj/question_generation).

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patil-suraj/question_generation/blob/master/question_generation.ipynb)

```python3
from pipelines import pipeline
nlp = pipeline("question-generation")
nlp("42 is the answer to life, universe and everything.")
=> [{'answer': '42', 'question': 'What is the answer to life, universe and everything?'}]
```
{"license": "mit", "tags": ["question-generation"], "datasets": ["squad"], "widget": [{"text": "<hl> 42 <hl> is the answer to life, the universe and everything. </s>"}, {"text": "Python is a programming language. It is developed by <hl> Guido Van Rossum <hl>. </s>"}, {"text": "Simple is better than <hl> complex <hl>. </s>"}]}
transformers pytorch jax t5 text2text-generation question-generation dataset:squad arxiv:1910.10683 license:mit autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 


## T5 for question-generation
This is t5-small model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens. 

You can play with the model using the inference API, just highlight the answer spans with '<hl>' tokens and end the text with '</s>'. For example

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

For more deatils see this repo.

### Model in action 

You'll need to clone the repo.

![Open In Colab](URL


## T5 for question-generation
This is t5-small model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens. 

You can play with the model using the inference API, just highlight the answer spans with '<hl>' tokens and end the text with '</s>'. For example

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

For more deatils see this repo. ### Model in action 

You'll need to clone the repo.

![Open In Colab](URL
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 ## T5 for question-generation
This is t5-small model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens. 

You can play with the model using the inference API, just highlight the answer spans with '<hl>' tokens and end the text with '</s>'. For example

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

For more deatils see this repo. ### Model in action 

You'll need to clone the repo.

![Open In Colab](URL
passage: TAGS
#transformers #pytorch #jax #t5 #text2text-generation #question-generation #dataset-squad #arxiv-1910.10683 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
## T5 for question-generation
This is t5-small model trained for answer aware question generation task. The answer spans are highlighted within the text with special highlight tokens. 

You can play with the model using the inference API, just highlight the answer spans with '<hl>' tokens and end the text with '</s>'. For example

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

For more deatils see this repo.### Model in action 

You'll need to clone the repo.

![Open In Colab](URL