
# codet5-small-Generate_Docstrings_for_Python-Condensed

This model is a fine-tuned version of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1444
- Rouge1: 0.3828
- Rouge2: 0.2214
- Rougel: 0.3583
- Rougelsum: 0.3661
- Gen Len: 12.6656

## Model description

This model is trained to predict the docstring (the output) for a function (the input).

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Generate%20Docstrings/Smol%20Dataset/Code_T5_Project-Small%20Checkpoint.ipynb

For this model, I trimmed some of the longer samples to quicken the pace of training on consumer hardware.

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

## Training and evaluation data

Dataset Source: calum/the-stack-smol-python-docstrings (from HuggingFace Datasets; https://huggingface.co/datasets/calum/the-stack-smol-python-docstrings)

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.9064        | 1.0   | 965  | 2.3096          | 0.3695 | 0.2098 | 0.3464 | 0.3529    | 11.7285 |
| 2.4836        | 2.0   | 1930 | 2.2051          | 0.38   | 0.2176 | 0.3554 | 0.3635    | 12.9401 |
| 2.3669        | 3.0   | 2895 | 2.1548          | 0.3842 | 0.2219 | 0.3595 | 0.3674    | 13.0029 |
| 2.3254        | 4.0   | 3860 | 2.1444          | 0.3828 | 0.2214 | 0.3583 | 0.3661    | 12.6656 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.12.1
- Datasets 2.9.0
- Tokenizers 0.12.1
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["calum/the-stack-smol-python-docstrings"], "metrics": ["rouge"], "pipeline_tag": "text2text-generation", "model-index": [{"name": "codet5-small-Generate_Docstrings_for_Python-Condensed", "results": []}]}
transformers pytorch tensorboard t5 text2text-generation generated_from_trainer en dataset:calum/the-stack-smol-python-docstrings license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #en #dataset-calum/the-stack-smol-python-docstrings #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

codet5-small-Generate\_Docstrings\_for\_Python-Condensed
========================================================


This model is a fine-tuned version of Salesforce/codet5-small on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 2.1444
* Rouge1: 0.3828
* Rouge2: 0.2214
* Rougel: 0.3583
* Rougelsum: 0.3661
* Gen Len: 12.6656


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


This model is trained to predict the docstring (the output) for a function (the input).


For more information on how it was created, check out the following link: URL


For this model, I trimmed some of the longer samples to quicken the pace of training on consumer hardware.


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


This model is intended to demonstrate my ability to solve a complex problem using technology.


Training and evaluation data
----------------------------


Dataset Source: calum/the-stack-smol-python-docstrings (from HuggingFace Datasets; URL


Training procedure
------------------


### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* 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: 4


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.12.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* 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: 4 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #en #dataset-calum/the-stack-smol-python-docstrings #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* 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: 4 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #en #dataset-calum/the-stack-smol-python-docstrings #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 2e-05
* 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: 4### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.12.1
* Datasets 2.9.0
* Tokenizers 0.12.1