

# t5-v1_1-base-finetuned-English-to-BASH

Created by: [Josh Shih](https://huggingface.co/Josh98), [Alex Sha](https://huggingface.co/alexsha), [Kevin Um](https://huggingface.co/kevinum) for EEP 596 - Natural Language Processing at University of Washington (Seattle).

This model is a fine-tuned version of [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) on a more balanced iteration of the [NL2BASH](https://github.com/TellinaTool/nl2bash/tree/master/data) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7958
- Nl2bash M: 0.6179
- Gen Len: 14.0821

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

This model was trained and evaluated using a custom iteration of [NL2BASH](https://github.com/TellinaTool/nl2bash/tree/master/data). 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](https://openai.com/blog/gpt-3-apps/) 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](https://s3.amazonaws.com/moonup/production/uploads/1677215336540-63d8b9876ac3104e50cd9634.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
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Nl2bash M | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 4.7401        | 1.0   | 561  | 1.6374          | 0.258     | 12.9715 |
| 1.9636        | 2.0   | 1122 | 1.2574          | 0.345     | 14.4728 |
| 1.5349        | 3.0   | 1683 | 1.0844          | 0.3873    | 14.5727 |
| 1.3565        | 4.0   | 2244 | 0.9777          | 0.4705    | 14.3506 |
| 1.2006        | 5.0   | 2805 | 0.8995          | 0.5536    | 14.2159 |
| 1.0931        | 6.0   | 3366 | 0.8622          | 0.5848    | 14.0883 |
| 1.0008        | 7.0   | 3927 | 0.8261          | 0.603     | 14.1401 |
| 0.952         | 8.0   | 4488 | 0.8146          | 0.6145    | 13.9322 |
| 0.8829        | 9.0   | 5049 | 0.8013          | 0.6185    | 13.9715 |
| 0.8657        | 10.0  | 5610 | 0.7958          | 0.6179    | 14.0821 |


### Framework versions

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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["nl2bash_m"], "model-index": [{"name": "t5-v1_1-base-finetuned-English-to-BASH", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

t5-v1\_1-base-finetuned-English-to-BASH
=======================================


Created by: Josh Shih, Alex Sha, Kevin Um for EEP 596 - Natural Language Processing at University of Washington (Seattle).


This model is a fine-tuned version of google/t5-v1\_1-base on a more balanced iteration of the NL2BASH dataset.
It achieves the following results on the evaluation set:


* Loss: 0.7958
* Nl2bash M: 0.6179
* Gen Len: 14.0821


Model description
-----------------


More information needed


Intended uses & limitations
---------------------------


More information needed


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
* 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



### 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
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### 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
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### 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