metadata
license: apache-2.0
base_model: EleutherAI/pythia-70m-deduped
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: pythia-70m-deduped-finetuned-github_cybersecurity_READMEs
results: []
pythia-70m-deduped-finetuned-github_cybersecurity_READMEs
This model is a fine-tuned version of EleutherAI/pythia-70m-deduped on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 7.1003
- Accuracy: 0.0669
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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.97 | 14 | 33.1751 | 0.0595 |
| No log | 2.0 | 29 | 32.9604 | 0.0635 |
| No log | 2.97 | 43 | 32.7028 | 0.0655 |
| No log | 4.0 | 58 | 32.3567 | 0.0674 |
| No log | 4.97 | 72 | 27.9492 | 0.0686 |
| No log | 6.0 | 87 | 6.4475 | 0.0665 |
| No log | 6.97 | 101 | 5.7208 | 0.0645 |
| No log | 8.0 | 116 | 5.4807 | 0.0690 |
| No log | 8.97 | 130 | 5.3024 | 0.0670 |
| No log | 10.0 | 145 | 5.1200 | 0.0640 |
| No log | 10.97 | 159 | 5.0031 | 0.0850 |
| No log | 12.0 | 174 | 4.9063 | 0.0845 |
| No log | 12.97 | 188 | 4.8488 | 0.0849 |
| No log | 14.0 | 203 | 4.7995 | 0.0827 |
| No log | 14.97 | 217 | 4.7393 | 0.0830 |
| No log | 16.0 | 232 | 4.6867 | 0.0812 |
| No log | 16.97 | 246 | 4.6346 | 0.0809 |
| No log | 18.0 | 261 | 4.5873 | 0.0801 |
| No log | 18.97 | 275 | 4.5435 | 0.0793 |
| No log | 20.0 | 290 | 4.4955 | 0.0780 |
| No log | 20.97 | 304 | 4.4505 | 0.0770 |
| No log | 22.0 | 319 | 4.4044 | 0.0760 |
| No log | 22.97 | 333 | 4.3258 | 0.0782 |
| No log | 24.0 | 348 | 4.2926 | 0.0760 |
| No log | 24.97 | 362 | 4.2353 | 0.0769 |
| No log | 26.0 | 377 | 4.2157 | 0.0751 |
| No log | 26.97 | 391 | 4.1705 | 0.0752 |
| No log | 28.0 | 406 | 4.1310 | 0.0754 |
| No log | 28.97 | 420 | 4.0981 | 0.0752 |
| No log | 30.0 | 435 | 4.0909 | 0.0733 |
| No log | 30.97 | 449 | 4.0291 | 0.0743 |
| No log | 32.0 | 464 | 4.0761 | 0.0721 |
| No log | 32.97 | 478 | 3.9794 | 0.0727 |
| No log | 34.0 | 493 | 3.9521 | 0.0733 |
| 8.0484 | 34.97 | 507 | 3.9421 | 0.0733 |
| 8.0484 | 36.0 | 522 | 3.9310 | 0.0727 |
| 8.0484 | 36.97 | 536 | 3.9142 | 0.0728 |
| 8.0484 | 38.0 | 551 | 3.9338 | 0.0723 |
| 8.0484 | 38.97 | 565 | 3.9189 | 0.0716 |
| 8.0484 | 40.0 | 580 | 3.9186 | 0.0718 |
| 8.0484 | 40.97 | 594 | 3.9216 | 0.0722 |
| 8.0484 | 42.0 | 609 | 3.8944 | 0.0718 |
| 8.0484 | 42.97 | 623 | 3.9038 | 0.0705 |
| 8.0484 | 44.0 | 638 | 3.9371 | 0.0707 |
| 8.0484 | 44.97 | 652 | 3.8716 | 0.0714 |
| 8.0484 | 46.0 | 667 | 3.9153 | 0.0705 |
| 8.0484 | 46.97 | 681 | 3.9540 | 0.0703 |
| 8.0484 | 48.0 | 696 | 3.9973 | 0.0706 |
| 8.0484 | 48.97 | 710 | 4.0011 | 0.0701 |
| 8.0484 | 50.0 | 725 | 4.0547 | 0.0696 |
| 8.0484 | 50.97 | 739 | 4.1899 | 0.0693 |
| 8.0484 | 52.0 | 754 | 4.1240 | 0.0707 |
| 8.0484 | 52.97 | 768 | 4.2480 | 0.0699 |
| 8.0484 | 54.0 | 783 | 4.2986 | 0.0691 |
| 8.0484 | 54.97 | 797 | 4.2061 | 0.0695 |
| 8.0484 | 56.0 | 812 | 4.3689 | 0.0695 |
| 8.0484 | 56.97 | 826 | 4.4121 | 0.0688 |
| 8.0484 | 58.0 | 841 | 4.4500 | 0.0686 |
| 8.0484 | 58.97 | 855 | 4.6004 | 0.0686 |
| 8.0484 | 60.0 | 870 | 4.6357 | 0.0680 |
| 8.0484 | 60.97 | 884 | 4.8464 | 0.0684 |
| 8.0484 | 62.0 | 899 | 4.6806 | 0.0687 |
| 8.0484 | 62.97 | 913 | 4.8374 | 0.0682 |
| 8.0484 | 64.0 | 928 | 4.8653 | 0.0679 |
| 8.0484 | 64.97 | 942 | 5.0424 | 0.0680 |
| 8.0484 | 66.0 | 957 | 5.1518 | 0.0680 |
| 8.0484 | 66.97 | 971 | 5.1240 | 0.0683 |
| 8.0484 | 68.0 | 986 | 5.1661 | 0.0678 |
| 1.9559 | 68.97 | 1000 | 5.3992 | 0.0687 |
| 1.9559 | 70.0 | 1015 | 5.4876 | 0.0680 |
| 1.9559 | 70.97 | 1029 | 5.5609 | 0.0683 |
| 1.9559 | 72.0 | 1044 | 5.6707 | 0.0679 |
| 1.9559 | 72.97 | 1058 | 5.7551 | 0.0667 |
| 1.9559 | 74.0 | 1073 | 5.9036 | 0.0675 |
| 1.9559 | 74.97 | 1087 | 6.1355 | 0.0665 |
| 1.9559 | 76.0 | 1102 | 6.2995 | 0.0661 |
| 1.9559 | 76.97 | 1116 | 6.2546 | 0.0677 |
| 1.9559 | 78.0 | 1131 | 6.3169 | 0.0672 |
| 1.9559 | 78.97 | 1145 | 6.3377 | 0.0669 |
| 1.9559 | 80.0 | 1160 | 6.4969 | 0.0673 |
| 1.9559 | 80.97 | 1174 | 6.6636 | 0.0664 |
| 1.9559 | 82.0 | 1189 | 6.7550 | 0.0672 |
| 1.9559 | 82.97 | 1203 | 6.7044 | 0.0661 |
| 1.9559 | 84.0 | 1218 | 6.7713 | 0.0669 |
| 1.9559 | 84.97 | 1232 | 6.8595 | 0.0668 |
| 1.9559 | 86.0 | 1247 | 6.9219 | 0.0663 |
| 1.9559 | 86.97 | 1261 | 6.9174 | 0.0666 |
| 1.9559 | 88.0 | 1276 | 6.9158 | 0.0667 |
| 1.9559 | 88.97 | 1290 | 6.9744 | 0.0670 |
| 1.9559 | 90.0 | 1305 | 6.9375 | 0.0669 |
| 1.9559 | 90.97 | 1319 | 6.9947 | 0.0668 |
| 1.9559 | 92.0 | 1334 | 7.0421 | 0.0671 |
| 1.9559 | 92.97 | 1348 | 7.0240 | 0.0666 |
| 1.9559 | 94.0 | 1363 | 7.0480 | 0.0669 |
| 1.9559 | 94.97 | 1377 | 7.0679 | 0.0668 |
| 1.9559 | 96.0 | 1392 | 7.1026 | 0.0670 |
| 1.9559 | 96.55 | 1400 | 7.1003 | 0.0669 |
Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2