
# RobustFill

GitHub: https://github.com/yeoedward/Robust-Fill

Original Paper: https://arxiv.org/pdf/1703.07469.pdf

The RobustFill network by Devlin et al. is trained for the following task -- based on a few example input-output string pairs, generate a program in a domain-specific language that transforms the given inputs into the given outputs. This program can then be used to transform unseen inputs. 

This repo contains a PyTorch checkpoint of the model and optimizer state dicts after `4 gpus x 195,200 minibatches x 32 batch_size = 24,985,600 samples` of training. 
Please see the demo notebook for an example of how to use it: [Demo Notebook](https://github.com/yeoedward/Robust-Fill/blob/master/demo.ipynb)
{"license": "mit", "tags": ["program synthesis"]}
program synthesis arxiv:1703.07469 license:mit region:us
TAGS
#program synthesis #arxiv-1703.07469 #license-mit #region-us 


# RobustFill

GitHub: URL

Original Paper: URL

The RobustFill network by Devlin et al. is trained for the following task -- based on a few example input-output string pairs, generate a program in a domain-specific language that transforms the given inputs into the given outputs. This program can then be used to transform unseen inputs. 

This repo contains a PyTorch checkpoint of the model and optimizer state dicts after '4 gpus x 195,200 minibatches x 32 batch_size = 24,985,600 samples' of training. 
Please see the demo notebook for an example of how to use it: Demo Notebook
# RobustFill

GitHub: URL

Original Paper: URL

The RobustFill network by Devlin et al. is trained for the following task -- based on a few example input-output string pairs, generate a program in a domain-specific language that transforms the given inputs into the given outputs. This program can then be used to transform unseen inputs. 

This repo contains a PyTorch checkpoint of the model and optimizer state dicts after '4 gpus x 195,200 minibatches x 32 batch_size = 24,985,600 samples' of training. 
Please see the demo notebook for an example of how to use it: Demo Notebook
TAGS
#program synthesis #arxiv-1703.07469 #license-mit #region-us 
 # RobustFill

GitHub: URL

Original Paper: URL

The RobustFill network by Devlin et al. is trained for the following task -- based on a few example input-output string pairs, generate a program in a domain-specific language that transforms the given inputs into the given outputs. This program can then be used to transform unseen inputs. 

This repo contains a PyTorch checkpoint of the model and optimizer state dicts after '4 gpus x 195,200 minibatches x 32 batch_size = 24,985,600 samples' of training. 
Please see the demo notebook for an example of how to use it: Demo Notebook
TAGS
#program synthesis #arxiv-1703.07469 #license-mit #region-us 
# RobustFill

GitHub: URL

Original Paper: URL

The RobustFill network by Devlin et al. is trained for the following task -- based on a few example input-output string pairs, generate a program in a domain-specific language that transforms the given inputs into the given outputs. This program can then be used to transform unseen inputs. 

This repo contains a PyTorch checkpoint of the model and optimizer state dicts after '4 gpus x 195,200 minibatches x 32 batch_size = 24,985,600 samples' of training. 
Please see the demo notebook for an example of how to use it: Demo Notebook