|
--- |
|
license: apache-2.0 |
|
base_model: distilgpt2 |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: distilgpt2-sd |
|
results: [] |
|
datasets: |
|
- Gustavosta/Stable-Diffusion-Prompts |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# distilgpt2-sd |
|
|
|
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on [Stable-Diffusion-Prompt](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts) dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.4481 |
|
|
|
## 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: 5e-05 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 3 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:-----:|:---------------:| |
|
| 3.36 | 0.05 | 500 | 2.8209 | |
|
| 2.8086 | 0.11 | 1000 | 2.5757 | |
|
| 2.6126 | 0.16 | 1500 | 2.4096 | |
|
| 2.4771 | 0.22 | 2000 | 2.3027 | |
|
| 2.3986 | 0.27 | 2500 | 2.2076 | |
|
| 2.3148 | 0.33 | 3000 | 2.1547 | |
|
| 2.237 | 0.38 | 3500 | 2.0825 | |
|
| 2.1731 | 0.43 | 4000 | 2.0334 | |
|
| 2.1256 | 0.49 | 4500 | 1.9806 | |
|
| 2.081 | 0.54 | 5000 | 1.9345 | |
|
| 2.0677 | 0.6 | 5500 | 1.9053 | |
|
| 1.9794 | 0.65 | 6000 | 1.8691 | |
|
| 2.0072 | 0.71 | 6500 | 1.8429 | |
|
| 1.9597 | 0.76 | 7000 | 1.8061 | |
|
| 1.9318 | 0.82 | 7500 | 1.7857 | |
|
| 1.9283 | 0.87 | 8000 | 1.7610 | |
|
| 1.8959 | 0.92 | 8500 | 1.7378 | |
|
| 1.8626 | 0.98 | 9000 | 1.7185 | |
|
| 1.8126 | 1.03 | 9500 | 1.7040 | |
|
| 1.7789 | 1.09 | 10000 | 1.6855 | |
|
| 1.7794 | 1.14 | 10500 | 1.6756 | |
|
| 1.7284 | 1.2 | 11000 | 1.6529 | |
|
| 1.7478 | 1.25 | 11500 | 1.6384 | |
|
| 1.7065 | 1.3 | 12000 | 1.6321 | |
|
| 1.7092 | 1.36 | 12500 | 1.6133 | |
|
| 1.6897 | 1.41 | 13000 | 1.6146 | |
|
| 1.6902 | 1.47 | 13500 | 1.5952 | |
|
| 1.6888 | 1.52 | 14000 | 1.5792 | |
|
| 1.6862 | 1.58 | 14500 | 1.5730 | |
|
| 1.6458 | 1.63 | 15000 | 1.5661 | |
|
| 1.6594 | 1.68 | 15500 | 1.5537 | |
|
| 1.6486 | 1.74 | 16000 | 1.5484 | |
|
| 1.6556 | 1.79 | 16500 | 1.5360 | |
|
| 1.6187 | 1.85 | 17000 | 1.5264 | |
|
| 1.6377 | 1.9 | 17500 | 1.5223 | |
|
| 1.6129 | 1.96 | 18000 | 1.5180 | |
|
| 1.6025 | 2.01 | 18500 | 1.5030 | |
|
| 1.5697 | 2.06 | 19000 | 1.4991 | |
|
| 1.5616 | 2.12 | 19500 | 1.5012 | |
|
| 1.558 | 2.17 | 20000 | 1.4984 | |
|
| 1.549 | 2.23 | 20500 | 1.4809 | |
|
| 1.5048 | 2.28 | 21000 | 1.4827 | |
|
| 1.5207 | 2.34 | 21500 | 1.4740 | |
|
| 1.5097 | 2.39 | 22000 | 1.4699 | |
|
| 1.541 | 2.45 | 22500 | 1.4701 | |
|
| 1.5355 | 2.5 | 23000 | 1.4637 | |
|
| 1.5318 | 2.55 | 23500 | 1.4609 | |
|
| 1.5352 | 2.61 | 24000 | 1.4580 | |
|
| 1.5202 | 2.66 | 24500 | 1.4566 | |
|
| 1.5073 | 2.72 | 25000 | 1.4547 | |
|
| 1.5462 | 2.77 | 25500 | 1.4520 | |
|
| 1.5347 | 2.83 | 26000 | 1.4491 | |
|
| 1.52 | 2.88 | 26500 | 1.4488 | |
|
| 1.5154 | 2.93 | 27000 | 1.4475 | |
|
| 1.4855 | 2.99 | 27500 | 1.4481 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.31.0 |
|
- Pytorch 2.0.1+cu118 |
|
- Datasets 2.13.1 |
|
- Tokenizers 0.13.3 |
