|
--- |
|
tags: |
|
- generated_from_trainer |
|
metrics: |
|
- bleu |
|
model-index: |
|
- name: h0-1 |
|
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. --> |
|
|
|
# h0-1 |
|
|
|
This model is a fine-tuned version of [microsoft/CodeGPT-small-py](https://huggingface.co/microsoft/CodeGPT-small-py) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3622 |
|
- Exact Match: 0.1970 |
|
- Bleu: 0.9193 |
|
- Codebleu: 0.7686 |
|
- Chrf: 93.5686 |
|
|
|
## 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: 4 |
|
- eval_batch_size: 4 |
|
- seed: 17 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: cosine |
|
- num_epochs: 200 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Exact Match | Bleu | Codebleu | Chrf | |
|
|:-------------:|:------:|:-----:|:---------------:|:-----------:|:------:|:--------:|:-------:| |
|
| 0.2482 | 11.94 | 1600 | 0.2828 | 0.1364 | 0.9012 | 0.7012 | 92.2247 | |
|
| 0.0203 | 23.88 | 3200 | 0.2968 | 0.1970 | 0.9114 | 0.7298 | 93.0236 | |
|
| 0.0082 | 35.82 | 4800 | 0.3049 | 0.1970 | 0.9125 | 0.7480 | 93.1997 | |
|
| 0.0049 | 47.76 | 6400 | 0.3190 | 0.1818 | 0.9125 | 0.7526 | 93.0967 | |
|
| 0.0038 | 59.7 | 8000 | 0.3289 | 0.1818 | 0.9117 | 0.7348 | 93.1293 | |
|
| 0.0024 | 71.64 | 9600 | 0.3358 | 0.1970 | 0.9142 | 0.7555 | 93.0747 | |
|
| 0.0022 | 83.58 | 11200 | 0.3379 | 0.1970 | 0.9164 | 0.7642 | 93.2931 | |
|
| 0.0013 | 95.52 | 12800 | 0.3444 | 0.2121 | 0.9189 | 0.7700 | 93.4456 | |
|
| 0.0009 | 107.46 | 14400 | 0.3408 | 0.1970 | 0.9188 | 0.7655 | 93.4808 | |
|
| 0.0006 | 119.4 | 16000 | 0.3522 | 0.1970 | 0.9177 | 0.7510 | 93.4061 | |
|
| 0.0003 | 131.34 | 17600 | 0.3589 | 0.2121 | 0.9178 | 0.7614 | 93.3980 | |
|
| 0.0002 | 143.28 | 19200 | 0.3562 | 0.2121 | 0.9179 | 0.7634 | 93.5130 | |
|
| 0.0002 | 155.22 | 20800 | 0.3624 | 0.1970 | 0.9208 | 0.7699 | 93.6707 | |
|
| 0.0001 | 167.16 | 22400 | 0.3608 | 0.1970 | 0.9193 | 0.7703 | 93.6082 | |
|
| 0.0001 | 179.1 | 24000 | 0.3620 | 0.1970 | 0.9190 | 0.7667 | 93.5154 | |
|
| 0.0001 | 191.04 | 25600 | 0.3622 | 0.1970 | 0.9193 | 0.7686 | 93.5686 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.24.0 |
|
- Pytorch 1.13.0 |
|
- Datasets 2.6.1 |
|
- Tokenizers 0.13.1 |
|
|
