---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
model-index:
- name: Salesforce-codet5-small-CodeXGLUE-CONCODE-adamw
  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-codet5-small-CodeXGLUE-CONCODE-adamw

This model is a fine-tuned version of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7666
- Exact Match: 0.163
- Rouge1: 0.5716
- Rouge2: 0.4046
- Rougel: 0.5536
- Rougelsum: 0.5614
- Bleu: 0.1335

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Exact Match | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu   |
|:-------------:|:-----:|:-----:|:---------------:|:-----------:|:------:|:------:|:------:|:---------:|:------:|
| 2.3935        | 0.16  | 500   | 0.9724          | 0.129       | 0.5286 | 0.3466 | 0.5098 | 0.5153    | 0.1127 |
| 0.8984        | 0.32  | 1000  | 0.8919          | 0.138       | 0.5463 | 0.3714 | 0.5285 | 0.5353    | 0.1200 |
| 0.8121        | 0.48  | 1500  | 0.8583          | 0.1455      | 0.5529 | 0.3787 | 0.5350 | 0.5426    | 0.1158 |
| 0.7598        | 0.64  | 2000  | 0.8437          | 0.1485      | 0.5541 | 0.3813 | 0.5355 | 0.5432    | 0.1197 |
| 0.7289        | 0.8   | 2500  | 0.8189          | 0.158       | 0.5597 | 0.3906 | 0.5416 | 0.5501    | 0.1222 |
| 0.7053        | 0.96  | 3000  | 0.8145          | 0.161       | 0.5572 | 0.3888 | 0.5392 | 0.5469    | 0.1222 |
| 0.6544        | 1.12  | 3500  | 0.7982          | 0.1565      | 0.5606 | 0.3920 | 0.5436 | 0.5517    | 0.1260 |
| 0.6334        | 1.28  | 4000  | 0.7974          | 0.1585      | 0.5633 | 0.3906 | 0.5448 | 0.5529    | 0.1284 |
| 0.6236        | 1.44  | 4500  | 0.7943          | 0.163       | 0.5639 | 0.3931 | 0.5455 | 0.5542    | 0.1275 |
| 0.6221        | 1.6   | 5000  | 0.7824          | 0.1655      | 0.5718 | 0.4011 | 0.5537 | 0.5621    | 0.1310 |
| 0.608         | 1.76  | 5500  | 0.7792          | 0.163       | 0.5664 | 0.3997 | 0.5490 | 0.5567    | 0.1314 |
| 0.5956        | 1.92  | 6000  | 0.7785          | 0.1605      | 0.5641 | 0.3981 | 0.5470 | 0.5546    | 0.1294 |
| 0.5701        | 2.08  | 6500  | 0.7800          | 0.157       | 0.5673 | 0.3955 | 0.5489 | 0.5568    | 0.1336 |
| 0.5378        | 2.24  | 7000  | 0.7720          | 0.1655      | 0.5686 | 0.4000 | 0.5504 | 0.5582    | 0.1308 |
| 0.541         | 2.4   | 7500  | 0.7709          | 0.1625      | 0.5699 | 0.3984 | 0.5511 | 0.5590    | 0.1313 |
| 0.5359        | 2.56  | 8000  | 0.7673          | 0.164       | 0.5697 | 0.4023 | 0.5521 | 0.5601    | 0.1332 |
| 0.5322        | 2.72  | 8500  | 0.7642          | 0.1665      | 0.5708 | 0.4033 | 0.5527 | 0.5606    | 0.1350 |
| 0.5387        | 2.88  | 9000  | 0.7622          | 0.159       | 0.5672 | 0.3988 | 0.5500 | 0.5573    | 0.1342 |
| 0.514         | 3.04  | 9500  | 0.7700          | 0.166       | 0.5722 | 0.4052 | 0.5546 | 0.5618    | 0.1352 |
| 0.4895        | 3.2   | 10000 | 0.7676          | 0.1615      | 0.5696 | 0.4016 | 0.5516 | 0.5591    | 0.1359 |
| 0.4827        | 3.36  | 10500 | 0.7665          | 0.162       | 0.5756 | 0.4072 | 0.5577 | 0.5656    | 0.1367 |
| 0.4814        | 3.52  | 11000 | 0.7700          | 0.1605      | 0.5709 | 0.4026 | 0.5528 | 0.5605    | 0.1334 |
| 0.4847        | 3.68  | 11500 | 0.7666          | 0.163       | 0.5716 | 0.4046 | 0.5536 | 0.5614    | 0.1335 |


### Framework versions

- Transformers 4.27.1
- Pytorch 1.12.1+cu113
- Datasets 2.10.1
- Tokenizers 0.13.2