gpt2-kl_01_05-hs_cn
This model is a fine-tuned version of gpt2-medium on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5387
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: 4
- seed: 21
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
73.5669 | 0.02 | 10 | 69.5838 |
46.1192 | 0.04 | 20 | 32.9319 |
13.5763 | 0.06 | 30 | 10.6437 |
5.6862 | 0.08 | 40 | 4.3509 |
2.8355 | 0.1 | 50 | 1.9914 |
1.4127 | 0.12 | 60 | 1.0386 |
1.139 | 0.14 | 70 | 0.8992 |
0.9191 | 0.16 | 80 | 0.7150 |
0.7454 | 0.18 | 90 | 0.7040 |
0.7465 | 0.2 | 100 | 0.6307 |
0.6444 | 0.22 | 110 | 0.6424 |
0.6783 | 0.24 | 120 | 0.6040 |
0.6724 | 0.26 | 130 | 0.6014 |
0.6898 | 0.28 | 140 | 0.6155 |
0.6583 | 0.3 | 150 | 0.5748 |
0.6234 | 0.32 | 160 | 0.5870 |
0.5572 | 0.34 | 170 | 0.5669 |
0.6596 | 0.36 | 180 | 0.5635 |
0.6763 | 0.38 | 190 | 0.5650 |
0.6112 | 0.4 | 200 | 0.5616 |
0.7173 | 0.42 | 210 | 0.5608 |
0.6714 | 0.44 | 220 | 0.5604 |
0.5898 | 0.46 | 230 | 0.5624 |
0.5849 | 0.48 | 240 | 0.5570 |
0.5825 | 0.5 | 250 | 0.5556 |
0.6123 | 0.52 | 260 | 0.5440 |
0.5956 | 0.54 | 270 | 0.5397 |
0.634 | 0.56 | 280 | 0.5404 |
0.6152 | 0.58 | 290 | 0.5387 |
0.5719 | 0.6 | 300 | 0.5396 |
0.587 | 0.62 | 310 | 0.5363 |
0.6913 | 0.64 | 320 | 0.5357 |
0.5504 | 0.66 | 330 | 0.5409 |
0.545 | 0.68 | 340 | 0.5359 |
0.558 | 0.7 | 350 | 0.5387 |
Framework versions
- Transformers 4.29.0.dev0
- Pytorch 1.12.0a0+bd13bc6
- Datasets 2.12.0
- Tokenizers 0.13.3
- Downloads last month
- 17
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.