Created by: Josh Shih, Alex Sha, Kevin Um for EEP 596 - Natural Language Processing at University of Washington (Seattle).
Model description
This model is a fine-tuned version of google/byt5-small on a more balanced iteration of the NL2BASH dataset.
It achieves the following results on the evaluation set:
Loss: 0.4850
Nl2bash M: 0.6376
Gen Len: 16.9946
Intended uses & limitations
Purpose: To generate bash commands from text input, and help people learn to use linux bash. This is a proof of concept model using transfer learning to fine-tune an existing language model and produce structured code instead of natural language.
Training and evaluation data
This model was trained and evaluated using a custom iteration of NL2BASH. The original NL2BASH dataset contains a large class imbalance with too many bash commands which begin with 'find'.
A maximum threshold was set to remove text/BASH pairs which exceeded the threshold, and GPT-3 API was used to generate text/BASH pairs for those below the threshold.
~5500 original text/BASH pairs and ~5700 generated text/BASH pairs were used, giving a total of ~11200 lines of text/BASH pairs. Shown below is the class distribution for the top-5 commands.
class_balanced.png
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 0.0001
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
