File size: 3,290 Bytes
740ed5f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
---
library_name: transformers
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: cadgpt-gpt2-train
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. -->
# cadgpt-gpt2-train
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0298
## 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.2987 | 0.0544 | 200 | 0.1816 |
| 0.1236 | 0.1088 | 400 | 0.0997 |
| 0.0876 | 0.1633 | 600 | 0.0692 |
| 0.0703 | 0.2177 | 800 | 0.0554 |
| 0.0601 | 0.2721 | 1000 | 0.0531 |
| 0.0553 | 0.3265 | 1200 | 0.0453 |
| 0.0514 | 0.3810 | 1400 | 0.0427 |
| 0.0473 | 0.4354 | 1600 | 0.0403 |
| 0.0463 | 0.4898 | 1800 | 0.0404 |
| 0.0461 | 0.5442 | 2000 | 0.0386 |
| 0.0428 | 0.5986 | 2200 | 0.0376 |
| 0.0428 | 0.6531 | 2400 | 0.0367 |
| 0.0395 | 0.7075 | 2600 | 0.0353 |
| 0.0396 | 0.7619 | 2800 | 0.0351 |
| 0.0388 | 0.8163 | 3000 | 0.0347 |
| 0.0383 | 0.8707 | 3200 | 0.0350 |
| 0.038 | 0.9252 | 3400 | 0.0359 |
| 0.0371 | 0.9796 | 3600 | 0.0343 |
| 0.0364 | 1.0340 | 3800 | 0.0328 |
| 0.0372 | 1.0884 | 4000 | 0.0331 |
| 0.0363 | 1.1429 | 4200 | 0.0324 |
| 0.0351 | 1.1973 | 4400 | 0.0334 |
| 0.0347 | 1.2517 | 4600 | 0.0317 |
| 0.0342 | 1.3061 | 4800 | 0.0315 |
| 0.0344 | 1.3605 | 5000 | 0.0314 |
| 0.0337 | 1.4150 | 5200 | 0.0309 |
| 0.0338 | 1.4694 | 5400 | 0.0310 |
| 0.0334 | 1.5238 | 5600 | 0.0308 |
| 0.0341 | 1.5782 | 5800 | 0.0306 |
| 0.033 | 1.6327 | 6000 | 0.0319 |
| 0.0335 | 1.6871 | 6200 | 0.0304 |
| 0.0322 | 1.7415 | 6400 | 0.0302 |
| 0.0329 | 1.7959 | 6600 | 0.0301 |
| 0.0336 | 1.8503 | 6800 | 0.0300 |
| 0.0328 | 1.9048 | 7000 | 0.0300 |
| 0.0331 | 1.9592 | 7200 | 0.0298 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
|