---
license: bsd-3-clause
base_model: Salesforce/codet5p-220m
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: Salesforce-codet5p-220m-finetuned-defect-detection
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Salesforce-codet5p-220m-finetuned-defect-detection

This model is a fine-tuned version of [Salesforce/codet5p-220m](https://huggingface.co/Salesforce/codet5p-220m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5331
- Accuracy: 0.7289
- Roc Auc: 0.7292
- Precision: 0.7152
- Recall: 0.7395

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 4711
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Roc Auc | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|:---------:|:------:|
| 0.6935        | 1.0   | 996  | 0.5783          | 0.6689   | 0.6657  | 0.7197    | 0.5277 |
| 0.5694        | 2.0   | 1993 | 0.5279          | 0.7013   | 0.7026  | 0.6723    | 0.7580 |
| 0.4812        | 3.0   | 2989 | 0.5058          | 0.7181   | 0.7179  | 0.7129    | 0.7081 |
| 0.4235        | 4.0   | 3986 | 0.5088          | 0.7292   | 0.7291  | 0.7213    | 0.7261 |
| 0.3658        | 5.0   | 4980 | 0.5331          | 0.7289   | 0.7292  | 0.7152    | 0.7395 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2