t5-small-finetuned-English-to-BASH

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 t5-small on a more balanced iteration of the NL2BASH dataset. It achieves the following results on the evaluation set:

• Loss: 0.9614
• nl2bash_m: 0.6192
• Gen Len: 14.1044

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.

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
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Framework versions

• Transformers 4.27.0.dev0
• Pytorch 1.13.1+cu116
• Datasets 2.10.0
• Tokenizers 0.13.2