|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- rouge |
|
- bleu |
|
model-index: |
|
- name: Salesforce-codet5-small-CodeXGLUE-CONCODE-adafactor |
|
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-adafactor |
|
|
|
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: 0.8118 |
|
- Exact Match: 0.1555 |
|
- Rouge1: 0.5580 |
|
- Rouge2: 0.3886 |
|
- Rougel: 0.5407 |
|
- Rougelsum: 0.5483 |
|
- Bleu: 0.1297 |
|
|
|
## 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: 0.0003 |
|
- 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:------:|:------:|:------:|:---------:|:------:| |
|
| 1.8525 | 0.16 | 500 | 0.9340 | 0.1435 | 0.5360 | 0.3596 | 0.5171 | 0.5238 | 0.1146 | |
|
| 0.8679 | 0.32 | 1000 | 0.9262 | 0.1405 | 0.5385 | 0.3659 | 0.5228 | 0.5294 | 0.1179 | |
|
| 0.8169 | 0.48 | 1500 | 0.8957 | 0.139 | 0.5372 | 0.3642 | 0.5192 | 0.5265 | 0.1135 | |
|
| 0.7734 | 0.64 | 2000 | 0.8827 | 0.14 | 0.5485 | 0.3706 | 0.5316 | 0.5381 | 0.1210 | |
|
| 0.743 | 0.8 | 2500 | 0.8647 | 0.155 | 0.5503 | 0.3833 | 0.5338 | 0.5411 | 0.1184 | |
|
| 0.72 | 0.96 | 3000 | 0.8661 | 0.1545 | 0.5460 | 0.3735 | 0.5284 | 0.5366 | 0.1162 | |
|
| 0.6539 | 1.12 | 3500 | 0.8591 | 0.156 | 0.5540 | 0.3841 | 0.5365 | 0.5444 | 0.1241 | |
|
| 0.6301 | 1.28 | 4000 | 0.8452 | 0.1485 | 0.5556 | 0.3794 | 0.5369 | 0.5451 | 0.1237 | |
|
| 0.6222 | 1.44 | 4500 | 0.8321 | 0.1585 | 0.5529 | 0.3818 | 0.5343 | 0.5430 | 0.1228 | |
|
| 0.6221 | 1.6 | 5000 | 0.8317 | 0.154 | 0.5664 | 0.3925 | 0.5481 | 0.5575 | 0.1289 | |
|
| 0.6067 | 1.76 | 5500 | 0.8228 | 0.1625 | 0.5607 | 0.3933 | 0.5438 | 0.5516 | 0.1299 | |
|
| 0.5927 | 1.92 | 6000 | 0.8179 | 0.156 | 0.5625 | 0.3942 | 0.5457 | 0.5526 | 0.1309 | |
|
| 0.5548 | 2.08 | 6500 | 0.8259 | 0.152 | 0.5582 | 0.3846 | 0.5402 | 0.5485 | 0.1314 | |
|
| 0.5146 | 2.24 | 7000 | 0.8328 | 0.1545 | 0.5605 | 0.3889 | 0.5429 | 0.5514 | 0.1299 | |
|
| 0.5193 | 2.4 | 7500 | 0.8197 | 0.1555 | 0.5604 | 0.3866 | 0.5431 | 0.5501 | 0.1268 | |
|
| 0.5172 | 2.56 | 8000 | 0.8118 | 0.1555 | 0.5580 | 0.3886 | 0.5407 | 0.5483 | 0.1297 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.27.1 |
|
- Pytorch 1.12.1+cu113 |
|
- Datasets 2.10.1 |
|
- Tokenizers 0.13.2 |
|
|
