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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: codeparrot-ds2 |
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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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# codeparrot-ds2 |
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GPT-2 style trained on a filtered set of The Stack, specific to data science related code. Things like pandas, numpy, matplotlib, etc. |
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- Loss: 1.0584 |
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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: 200 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 2.2038 | 0.01 | 500 | 2.1062 | |
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| 2.0551 | 0.02 | 1000 | 2.0109 | |
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| 1.9622 | 0.02 | 1500 | 1.9219 | |
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| 1.9512 | 0.03 | 2000 | 1.8461 | |
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| 1.8817 | 0.04 | 2500 | 1.7903 | |
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| 1.8341 | 0.05 | 3000 | 1.7401 | |
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| 1.7877 | 0.05 | 3500 | 1.7022 | |
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| 1.7586 | 0.06 | 4000 | 1.6694 | |
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| 1.7271 | 0.07 | 4500 | 1.6457 | |
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| 1.7034 | 0.08 | 5000 | 1.6193 | |
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| 1.6756 | 0.08 | 5500 | 1.5978 | |
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| 1.6576 | 0.09 | 6000 | 1.5772 | |
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| 1.6377 | 0.1 | 6500 | 1.5611 | |
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| 1.6211 | 0.11 | 7000 | 1.5453 | |
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| 1.6033 | 0.11 | 7500 | 1.5317 | |
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| 1.591 | 0.12 | 8000 | 1.5193 | |
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| 1.5765 | 0.13 | 8500 | 1.5053 | |
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| 1.5661 | 0.14 | 9000 | 1.4966 | |
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| 1.5548 | 0.15 | 9500 | 1.4846 | |
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| 1.5429 | 0.15 | 10000 | 1.4729 | |
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| 1.5347 | 0.16 | 10500 | 1.4641 | |
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| 1.5215 | 0.17 | 11000 | 1.4557 | |
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| 1.5151 | 0.18 | 11500 | 1.4454 | |
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| 1.5059 | 0.18 | 12000 | 1.4381 | |
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| 1.499 | 0.19 | 12500 | 1.4288 | |
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| 1.4906 | 0.2 | 13000 | 1.4210 | |
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| 1.4849 | 0.21 | 13500 | 1.4143 | |
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| 1.4765 | 0.21 | 14000 | 1.4085 | |
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| 1.4708 | 0.22 | 14500 | 1.4026 | |
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| 1.4602 | 0.23 | 15000 | 1.3936 | |
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| 1.4533 | 0.24 | 15500 | 1.3896 | |
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| 1.4523 | 0.25 | 16000 | 1.3818 | |
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| 1.4415 | 0.25 | 16500 | 1.3748 | |
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| 1.4417 | 0.26 | 17000 | 1.3701 | |
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| 1.4311 | 0.27 | 17500 | 1.3645 | |
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| 1.4282 | 0.28 | 18000 | 1.3585 | |
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| 1.4223 | 0.28 | 18500 | 1.3531 | |
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| 1.4165 | 0.29 | 19000 | 1.3473 | |
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| 1.4105 | 0.3 | 19500 | 1.3419 | |
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| 1.3993 | 0.31 | 20000 | 1.3374 | |
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| 1.4034 | 0.31 | 20500 | 1.3322 | |
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| 1.3982 | 0.32 | 21000 | 1.3278 | |
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| 1.3951 | 0.33 | 21500 | 1.3225 | |
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| 1.3806 | 0.34 | 22000 | 1.3180 | |
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| 1.3781 | 0.34 | 22500 | 1.3121 | |
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| 1.3761 | 0.35 | 23000 | 1.3082 | |
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| 1.3662 | 0.36 | 23500 | 1.3038 | |
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| 1.3631 | 0.37 | 24000 | 1.2995 | |
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| 1.3549 | 0.38 | 24500 | 1.2955 | |
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| 1.3577 | 0.38 | 25000 | 1.2912 | |
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| 1.3498 | 0.39 | 25500 | 1.2851 | |
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| 1.3428 | 0.4 | 26000 | 1.2807 | |
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| 1.342 | 0.41 | 26500 | 1.2768 | |
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| 1.3365 | 0.41 | 27000 | 1.2720 | |
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| 1.3313 | 0.42 | 27500 | 1.2678 | |
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| 1.3309 | 0.43 | 28000 | 1.2629 | |
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| 1.3221 | 0.44 | 28500 | 1.2594 | |
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| 1.3214 | 0.44 | 29000 | 1.2558 | |
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| 1.3099 | 0.45 | 29500 | 1.2510 | |
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| 1.31 | 0.46 | 30000 | 1.2449 | |
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| 1.31 | 0.47 | 30500 | 1.2414 | |
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| 1.305 | 0.48 | 31000 | 1.2390 | |
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| 1.2975 | 0.48 | 31500 | 1.2358 | |
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| 1.2882 | 0.49 | 32000 | 1.2311 | |
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| 1.2831 | 0.5 | 32500 | 1.2251 | |
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| 1.2836 | 0.51 | 33000 | 1.2212 | |
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| 1.2817 | 0.51 | 33500 | 1.2178 | |
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| 1.2772 | 0.52 | 34000 | 1.2130 | |
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| 1.2651 | 0.53 | 34500 | 1.2080 | |
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| 1.2683 | 0.54 | 35000 | 1.2048 | |
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| 1.2581 | 0.54 | 35500 | 1.1999 | |
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| 1.263 | 0.55 | 36000 | 1.1972 | |
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| 1.255 | 0.56 | 36500 | 1.1924 | |
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| 1.2466 | 0.57 | 37000 | 1.1884 | |
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| 1.2448 | 0.57 | 37500 | 1.1860 | |
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| 1.2413 | 0.58 | 38000 | 1.1804 | |
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| 1.2362 | 0.59 | 38500 | 1.1782 | |
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| 1.2309 | 0.6 | 39000 | 1.1732 | |
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| 1.2289 | 0.61 | 39500 | 1.1687 | |
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| 1.2208 | 0.61 | 40000 | 1.1649 | |
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| 1.2225 | 0.62 | 40500 | 1.1605 | |
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| 1.2178 | 0.63 | 41000 | 1.1555 | |
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| 1.208 | 0.64 | 41500 | 1.1533 | |
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| 1.2069 | 0.64 | 42000 | 1.1490 | |
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| 1.206 | 0.65 | 42500 | 1.1453 | |
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| 1.2013 | 0.66 | 43000 | 1.1414 | |
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| 1.2003 | 0.67 | 43500 | 1.1374 | |
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| 1.1867 | 0.67 | 44000 | 1.1337 | |
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| 1.187 | 0.68 | 44500 | 1.1302 | |
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| 1.188 | 0.69 | 45000 | 1.1270 | |
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| 1.179 | 0.7 | 45500 | 1.1237 | |
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| 1.1866 | 0.71 | 46000 | 1.1204 | |
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| 1.173 | 0.71 | 46500 | 1.1173 | |
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| 1.1706 | 0.72 | 47000 | 1.1134 | |
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| 1.1645 | 0.73 | 47500 | 1.1099 | |
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| 1.1641 | 0.74 | 48000 | 1.1063 | |
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| 1.1623 | 0.74 | 48500 | 1.1032 | |
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| 1.1561 | 0.75 | 49000 | 1.1006 | |
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| 1.1531 | 0.76 | 49500 | 1.0977 | |
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| 1.1569 | 0.77 | 50000 | 1.0950 | |
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| 1.1505 | 0.77 | 50500 | 1.0927 | |
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| 1.1473 | 0.78 | 51000 | 1.0902 | |
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| 1.1428 | 0.79 | 51500 | 1.0870 | |
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| 1.1412 | 0.8 | 52000 | 1.0844 | |
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| 1.1452 | 0.8 | 52500 | 1.0823 | |
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| 1.1391 | 0.81 | 53000 | 1.0805 | |
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| 1.1329 | 0.82 | 53500 | 1.0783 | |
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| 1.1295 | 0.83 | 54000 | 1.0764 | |
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| 1.125 | 0.84 | 54500 | 1.0746 | |
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| 1.1295 | 0.84 | 55000 | 1.0730 | |
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| 1.1247 | 0.85 | 55500 | 1.0711 | |
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| 1.1225 | 0.86 | 56000 | 1.0696 | |
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| 1.1235 | 0.87 | 56500 | 1.0680 | |
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| 1.1192 | 0.87 | 57000 | 1.0670 | |
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| 1.1189 | 0.88 | 57500 | 1.0654 | |
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| 1.1196 | 0.89 | 58000 | 1.0646 | |
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| 1.1152 | 0.9 | 58500 | 1.0635 | |
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| 1.1133 | 0.9 | 59000 | 1.0628 | |
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| 1.1126 | 0.91 | 59500 | 1.0619 | |
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| 1.1142 | 0.92 | 60000 | 1.0610 | |
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| 1.1112 | 0.93 | 60500 | 1.0605 | |
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| 1.1137 | 0.93 | 61000 | 1.0599 | |
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| 1.1127 | 0.94 | 61500 | 1.0595 | |
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| 1.1111 | 0.95 | 62000 | 1.0592 | |
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| 1.1121 | 0.96 | 62500 | 1.0588 | |
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| 1.1114 | 0.97 | 63000 | 1.0587 | |
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| 1.1121 | 0.97 | 63500 | 1.0585 | |
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| 1.1078 | 0.98 | 64000 | 1.0584 | |
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| 1.1104 | 0.99 | 64500 | 1.0584 | |
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| 1.1057 | 1.0 | 65000 | 1.0584 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- Pytorch 1.13.1 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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