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--- |
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license: mit |
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base_model: gpt2 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: python-gpt2 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# python-gpt2 |
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This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.1448 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0005 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 256 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 1 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 9.2956 | 0.0138 | 25 | 7.9483 | |
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| 6.8319 | 0.0275 | 50 | 6.0463 | |
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| 5.653 | 0.0413 | 75 | 5.3905 | |
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| 5.0998 | 0.0551 | 100 | 5.0523 | |
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| 4.7296 | 0.0688 | 125 | 4.7295 | |
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| 4.4676 | 0.0826 | 150 | 4.4801 | |
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| 4.2285 | 0.0964 | 175 | 4.2580 | |
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| 4.0335 | 0.1101 | 200 | 4.0891 | |
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| 3.8654 | 0.1239 | 225 | 3.9376 | |
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| 3.7442 | 0.1377 | 250 | 3.8222 | |
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| 3.6155 | 0.1514 | 275 | 3.7006 | |
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| 3.4805 | 0.1652 | 300 | 3.5997 | |
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| 3.3804 | 0.1790 | 325 | 3.4840 | |
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| 3.3074 | 0.1927 | 350 | 3.3887 | |
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| 3.1737 | 0.2065 | 375 | 3.2711 | |
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| 3.0593 | 0.2203 | 400 | 3.1535 | |
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| 2.9634 | 0.2340 | 425 | 3.0443 | |
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| 2.887 | 0.2478 | 450 | 2.9574 | |
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| 2.7808 | 0.2616 | 475 | 2.8775 | |
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| 2.7117 | 0.2753 | 500 | 2.8190 | |
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| 2.6611 | 0.2891 | 525 | 2.7515 | |
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| 2.6141 | 0.3029 | 550 | 2.7097 | |
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| 2.5752 | 0.3167 | 575 | 2.6704 | |
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| 2.5038 | 0.3304 | 600 | 2.6307 | |
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| 2.4852 | 0.3442 | 625 | 2.6004 | |
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| 2.4638 | 0.3580 | 650 | 2.5696 | |
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| 2.4362 | 0.3717 | 675 | 2.5343 | |
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| 2.3896 | 0.3855 | 700 | 2.5131 | |
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| 2.3669 | 0.3993 | 725 | 2.4886 | |
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| 2.3174 | 0.4130 | 750 | 2.4695 | |
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| 2.3152 | 0.4268 | 775 | 2.4478 | |
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| 2.2916 | 0.4406 | 800 | 2.4271 | |
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| 2.2743 | 0.4543 | 825 | 2.4166 | |
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| 2.2555 | 0.4681 | 850 | 2.3959 | |
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| 2.2545 | 0.4819 | 875 | 2.3794 | |
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| 2.2291 | 0.4956 | 900 | 2.3645 | |
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| 2.2032 | 0.5094 | 925 | 2.3499 | |
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| 2.1842 | 0.5232 | 950 | 2.3382 | |
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| 2.1505 | 0.5369 | 975 | 2.3263 | |
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| 2.1668 | 0.5507 | 1000 | 2.3147 | |
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| 2.1649 | 0.5645 | 1025 | 2.3072 | |
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| 2.1427 | 0.5782 | 1050 | 2.2926 | |
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| 2.1051 | 0.5920 | 1075 | 2.2799 | |
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| 2.0792 | 0.6058 | 1100 | 2.2708 | |
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| 2.1171 | 0.6195 | 1125 | 2.2570 | |
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| 2.1012 | 0.6333 | 1150 | 2.2470 | |
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| 2.0853 | 0.6471 | 1175 | 2.2405 | |
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| 2.0786 | 0.6608 | 1200 | 2.2312 | |
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| 2.0664 | 0.6746 | 1225 | 2.2238 | |
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| 2.0706 | 0.6884 | 1250 | 2.2183 | |
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| 2.0557 | 0.7021 | 1275 | 2.2102 | |
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| 2.0404 | 0.7159 | 1300 | 2.2042 | |
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| 2.0493 | 0.7297 | 1325 | 2.1978 | |
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| 2.0373 | 0.7434 | 1350 | 2.1907 | |
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| 2.0093 | 0.7572 | 1375 | 2.1837 | |
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| 2.0228 | 0.7710 | 1400 | 2.1819 | |
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| 2.0147 | 0.7847 | 1425 | 2.1739 | |
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| 2.0206 | 0.7985 | 1450 | 2.1694 | |
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| 2.0156 | 0.8123 | 1475 | 2.1671 | |
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| 2.0126 | 0.8260 | 1500 | 2.1622 | |
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| 1.9834 | 0.8398 | 1525 | 2.1598 | |
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| 2.0182 | 0.8536 | 1550 | 2.1558 | |
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| 1.9876 | 0.8674 | 1575 | 2.1543 | |
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| 1.9914 | 0.8811 | 1600 | 2.1515 | |
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| 1.9933 | 0.8949 | 1625 | 2.1498 | |
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| 1.9945 | 0.9087 | 1650 | 2.1483 | |
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| 1.9733 | 0.9224 | 1675 | 2.1470 | |
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| 1.9778 | 0.9362 | 1700 | 2.1467 | |
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| 1.983 | 0.9500 | 1725 | 2.1454 | |
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| 1.9716 | 0.9637 | 1750 | 2.1453 | |
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| 1.9668 | 0.9775 | 1775 | 2.1449 | |
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| 1.9733 | 0.9913 | 1800 | 2.1448 | |
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### Framework versions |
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- Transformers 4.40.1 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.19.0 |
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- Tokenizers 0.19.1 |
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