Model save
Browse files- README.md +191 -53
- all_results.json +6 -6
- train_results.json +6 -6
README.md
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---
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library_name: peft
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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:
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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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-
#
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This model is a fine-tuned version of [gpt2
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It achieves the following results on the evaluation set:
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-
- Loss: 0.
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## Model description
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The following hyperparameters were used during training:
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- learning_rate: 5e-05
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- train_batch_size:
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- eval_batch_size:
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size:
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.03
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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### Framework versions
|
|
|
|
| 1 |
---
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| 2 |
library_name: peft
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| 3 |
license: mit
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| 4 |
+
base_model: gpt2
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| 5 |
tags:
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| 6 |
- generated_from_trainer
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| 7 |
model-index:
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| 8 |
+
- name: Se124M100KInfPrompt
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results: []
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| 10 |
---
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| 11 |
|
| 12 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 13 |
should probably proofread and complete it, then remove this comment. -->
|
| 14 |
|
| 15 |
+
# Se124M100KInfPrompt
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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: 0.3662
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|
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## Model description
|
| 22 |
|
|
|
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The following hyperparameters were used during training:
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| 38 |
- learning_rate: 5e-05
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+
- train_batch_size: 16
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+
- eval_batch_size: 16
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- seed: 42
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- gradient_accumulation_steps: 4
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+
- total_train_batch_size: 64
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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| 45 |
- lr_scheduler_type: cosine
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| 46 |
- lr_scheduler_warmup_ratio: 0.03
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| 50 |
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| 51 |
| Training Loss | Epoch | Step | Validation Loss |
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| 52 |
|:-------------:|:------:|:----:|:---------------:|
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+
| 2.8209 | 0.0164 | 20 | 2.4755 |
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| 2.7724 | 0.0327 | 40 | 2.4510 |
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| 2.7076 | 0.0491 | 60 | 2.3560 |
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| 2.5366 | 0.0655 | 80 | 2.1926 |
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| 2.2739 | 0.0818 | 100 | 1.9387 |
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| 1.9339 | 0.0982 | 120 | 1.6141 |
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| 1.5753 | 0.1146 | 140 | 1.2455 |
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| 1.254 | 0.1309 | 160 | 0.9377 |
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| 1.0138 | 0.1473 | 180 | 0.7895 |
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| 0.866 | 0.1637 | 200 | 0.6805 |
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| 0.7571 | 0.1800 | 220 | 0.5998 |
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| 0.6901 | 0.1964 | 240 | 0.5554 |
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| 0.629 | 0.2128 | 260 | 0.5310 |
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| 0.5879 | 0.2291 | 280 | 0.5076 |
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| 0.5703 | 0.2455 | 300 | 0.4930 |
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| 0.5546 | 0.2619 | 320 | 0.4845 |
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| 0.5419 | 0.2782 | 340 | 0.4762 |
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| 0.5179 | 0.2946 | 360 | 0.4656 |
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| 0.5211 | 0.3110 | 380 | 0.4595 |
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| 0.5039 | 0.3273 | 400 | 0.4543 |
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| 0.4992 | 0.3437 | 420 | 0.4524 |
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| 0.4937 | 0.3601 | 440 | 0.4477 |
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| 0.4801 | 0.3764 | 460 | 0.4409 |
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| 0.4805 | 0.3928 | 480 | 0.4380 |
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| 0.4805 | 0.4092 | 500 | 0.4354 |
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| 0.468 | 0.4255 | 520 | 0.4343 |
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| 0.4759 | 0.4419 | 540 | 0.4319 |
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| 0.4614 | 0.4583 | 560 | 0.4284 |
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| 0.4622 | 0.4746 | 580 | 0.4272 |
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| 0.4608 | 0.4910 | 600 | 0.4267 |
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| 0.4621 | 0.5074 | 620 | 0.4236 |
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| 0.4569 | 0.5237 | 640 | 0.4238 |
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| 0.4519 | 0.5401 | 660 | 0.4219 |
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| 0.4478 | 0.5565 | 680 | 0.4189 |
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| 0.4524 | 0.5728 | 700 | 0.4167 |
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| 0.4489 | 0.5892 | 720 | 0.4147 |
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| 0.4452 | 0.6056 | 740 | 0.4150 |
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| 0.4424 | 0.6219 | 760 | 0.4118 |
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| 0.4355 | 0.6383 | 780 | 0.4117 |
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| 0.4396 | 0.6547 | 800 | 0.4112 |
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| 0.4432 | 0.6710 | 820 | 0.4078 |
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| 0.44 | 0.6874 | 840 | 0.4051 |
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| 0.4341 | 0.7038 | 860 | 0.4050 |
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| 0.4425 | 0.7201 | 880 | 0.4018 |
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| 0.4387 | 0.7365 | 900 | 0.4016 |
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| 0.4369 | 0.7529 | 920 | 0.4031 |
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| 0.437 | 0.7692 | 940 | 0.3967 |
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| 0.4314 | 0.7856 | 960 | 0.4007 |
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| 0.4371 | 0.8020 | 980 | 0.3943 |
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| 0.4364 | 0.8183 | 1000 | 0.3986 |
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| 0.4292 | 0.8347 | 1020 | 0.3970 |
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| 0.427 | 0.8511 | 1040 | 0.3951 |
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| 0.431 | 0.8674 | 1060 | 0.3941 |
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| 0.4327 | 0.8838 | 1080 | 0.3958 |
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| 0.4263 | 0.9002 | 1100 | 0.3930 |
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| 0.429 | 0.9165 | 1120 | 0.3901 |
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| 0.4277 | 0.9329 | 1140 | 0.3907 |
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| 0.4251 | 0.9493 | 1160 | 0.3906 |
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| 0.4279 | 0.9656 | 1180 | 0.3891 |
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| 0.4249 | 0.9820 | 1200 | 0.3884 |
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| 0.4213 | 0.9984 | 1220 | 0.3891 |
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| 0.4192 | 1.0147 | 1240 | 0.3870 |
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| 0.4263 | 1.0311 | 1260 | 0.3852 |
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| 0.4219 | 1.0475 | 1280 | 0.3897 |
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| 0.4256 | 1.0638 | 1300 | 0.3846 |
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| 0.4129 | 1.0802 | 1320 | 0.3855 |
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| 0.4184 | 1.0966 | 1340 | 0.3841 |
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| 120 |
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| 0.4207 | 1.1129 | 1360 | 0.3835 |
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| 121 |
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| 0.418 | 1.1293 | 1380 | 0.3808 |
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| 0.4153 | 1.1457 | 1400 | 0.3827 |
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| 0.4247 | 1.1620 | 1420 | 0.3812 |
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| 0.421 | 1.1784 | 1440 | 0.3807 |
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| 0.4127 | 1.1948 | 1460 | 0.3802 |
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| 0.4233 | 1.2111 | 1480 | 0.3794 |
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| 0.4235 | 1.2275 | 1500 | 0.3782 |
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| 128 |
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| 0.4184 | 1.2439 | 1520 | 0.3785 |
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| 129 |
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| 0.4171 | 1.2602 | 1540 | 0.3796 |
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| 0.4181 | 1.2766 | 1560 | 0.3811 |
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| 0.4126 | 1.2930 | 1580 | 0.3780 |
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| 0.4188 | 1.3093 | 1600 | 0.3760 |
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| 0.4162 | 1.3257 | 1620 | 0.3769 |
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| 0.4192 | 1.3421 | 1640 | 0.3770 |
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| 0.4153 | 1.3584 | 1660 | 0.3763 |
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| 0.4187 | 1.3748 | 1680 | 0.3737 |
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| 0.4138 | 1.3912 | 1700 | 0.3755 |
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| 0.4115 | 1.4075 | 1720 | 0.3755 |
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| 0.4118 | 1.4239 | 1740 | 0.3756 |
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| 0.4036 | 1.4403 | 1760 | 0.3742 |
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| 141 |
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| 0.4161 | 1.4566 | 1780 | 0.3731 |
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| 0.4102 | 1.4730 | 1800 | 0.3740 |
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| 143 |
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| 0.4118 | 1.4894 | 1820 | 0.3731 |
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| 144 |
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| 0.4102 | 1.5057 | 1840 | 0.3732 |
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| 145 |
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| 0.4143 | 1.5221 | 1860 | 0.3744 |
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| 146 |
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| 0.4118 | 1.5385 | 1880 | 0.3729 |
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| 147 |
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| 0.4179 | 1.5548 | 1900 | 0.3721 |
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| 148 |
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| 0.4092 | 1.5712 | 1920 | 0.3716 |
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| 149 |
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| 0.4109 | 1.5876 | 1940 | 0.3726 |
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| 0.4137 | 1.6039 | 1960 | 0.3713 |
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| 151 |
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| 0.4067 | 1.6203 | 1980 | 0.3714 |
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| 152 |
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| 0.4131 | 1.6367 | 2000 | 0.3725 |
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| 153 |
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| 0.4103 | 1.6530 | 2020 | 0.3702 |
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| 154 |
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| 0.4044 | 1.6694 | 2040 | 0.3711 |
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| 155 |
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| 0.4105 | 1.6858 | 2060 | 0.3727 |
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| 156 |
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| 0.4063 | 1.7021 | 2080 | 0.3712 |
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| 157 |
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| 0.4109 | 1.7185 | 2100 | 0.3709 |
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| 158 |
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| 0.4114 | 1.7349 | 2120 | 0.3706 |
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| 159 |
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| 0.4148 | 1.7512 | 2140 | 0.3711 |
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| 160 |
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| 0.4081 | 1.7676 | 2160 | 0.3693 |
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| 161 |
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| 0.4062 | 1.7840 | 2180 | 0.3694 |
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| 162 |
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| 0.4152 | 1.8003 | 2200 | 0.3699 |
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| 163 |
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| 0.4043 | 1.8167 | 2220 | 0.3686 |
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| 164 |
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| 0.4046 | 1.8331 | 2240 | 0.3705 |
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| 165 |
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| 0.4136 | 1.8494 | 2260 | 0.3684 |
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| 166 |
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| 0.4073 | 1.8658 | 2280 | 0.3701 |
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| 167 |
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| 0.4089 | 1.8822 | 2300 | 0.3689 |
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| 168 |
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| 0.4075 | 1.8985 | 2320 | 0.3679 |
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| 169 |
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| 0.409 | 1.9149 | 2340 | 0.3694 |
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| 170 |
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| 0.4096 | 1.9313 | 2360 | 0.3677 |
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| 171 |
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| 0.4114 | 1.9476 | 2380 | 0.3686 |
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| 172 |
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| 0.4083 | 1.9640 | 2400 | 0.3676 |
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| 173 |
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| 0.4066 | 1.9804 | 2420 | 0.3696 |
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| 174 |
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| 0.4053 | 1.9967 | 2440 | 0.3677 |
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| 175 |
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| 0.4087 | 2.0131 | 2460 | 0.3688 |
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| 176 |
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| 0.4055 | 2.0295 | 2480 | 0.3680 |
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| 177 |
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| 0.4103 | 2.0458 | 2500 | 0.3678 |
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| 178 |
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| 0.4031 | 2.0622 | 2520 | 0.3685 |
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| 179 |
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| 0.4111 | 2.0786 | 2540 | 0.3674 |
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| 180 |
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| 0.413 | 2.0949 | 2560 | 0.3675 |
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| 0.4135 | 2.1113 | 2580 | 0.3674 |
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| 182 |
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| 0.4085 | 2.1277 | 2600 | 0.3664 |
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| 183 |
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| 0.4029 | 2.1440 | 2620 | 0.3683 |
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| 184 |
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| 0.4023 | 2.1604 | 2640 | 0.3677 |
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| 185 |
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| 0.4087 | 2.1768 | 2660 | 0.3673 |
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| 186 |
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| 0.4088 | 2.1931 | 2680 | 0.3678 |
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| 187 |
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| 0.4064 | 2.2095 | 2700 | 0.3664 |
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| 188 |
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| 0.4067 | 2.2259 | 2720 | 0.3669 |
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| 189 |
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| 0.4047 | 2.2422 | 2740 | 0.3662 |
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| 190 |
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| 0.4069 | 2.2586 | 2760 | 0.3666 |
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| 191 |
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| 0.4028 | 2.2750 | 2780 | 0.3663 |
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| 192 |
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| 0.4101 | 2.2913 | 2800 | 0.3664 |
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| 193 |
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| 0.4061 | 2.3077 | 2820 | 0.3663 |
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| 194 |
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| 0.4056 | 2.3241 | 2840 | 0.3657 |
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| 195 |
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| 0.4073 | 2.3404 | 2860 | 0.3660 |
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| 196 |
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| 0.4096 | 2.3568 | 2880 | 0.3665 |
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| 197 |
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| 0.4034 | 2.3732 | 2900 | 0.3667 |
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| 198 |
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| 0.4067 | 2.3895 | 2920 | 0.3668 |
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| 199 |
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| 0.4032 | 2.4059 | 2940 | 0.3673 |
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| 0.4082 | 2.4223 | 2960 | 0.3666 |
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| 0.4048 | 2.4386 | 2980 | 0.3660 |
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| 0.4058 | 2.4550 | 3000 | 0.3661 |
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| 203 |
+
| 0.4066 | 2.4714 | 3020 | 0.3663 |
|
| 204 |
+
| 0.4128 | 2.4877 | 3040 | 0.3662 |
|
| 205 |
+
| 0.4104 | 2.5041 | 3060 | 0.3658 |
|
| 206 |
+
| 0.4057 | 2.5205 | 3080 | 0.3658 |
|
| 207 |
+
| 0.408 | 2.5368 | 3100 | 0.3660 |
|
| 208 |
+
| 0.4053 | 2.5532 | 3120 | 0.3660 |
|
| 209 |
+
| 0.3998 | 2.5696 | 3140 | 0.3664 |
|
| 210 |
+
| 0.4007 | 2.5859 | 3160 | 0.3659 |
|
| 211 |
+
| 0.402 | 2.6023 | 3180 | 0.3660 |
|
| 212 |
+
| 0.4017 | 2.6187 | 3200 | 0.3660 |
|
| 213 |
+
| 0.4069 | 2.6350 | 3220 | 0.3659 |
|
| 214 |
+
| 0.4028 | 2.6514 | 3240 | 0.3662 |
|
| 215 |
+
| 0.4014 | 2.6678 | 3260 | 0.3663 |
|
| 216 |
+
| 0.4023 | 2.6841 | 3280 | 0.3666 |
|
| 217 |
+
| 0.4025 | 2.7005 | 3300 | 0.3668 |
|
| 218 |
+
| 0.4027 | 2.7169 | 3320 | 0.3661 |
|
| 219 |
+
| 0.404 | 2.7332 | 3340 | 0.3659 |
|
| 220 |
+
| 0.4064 | 2.7496 | 3360 | 0.3663 |
|
| 221 |
+
| 0.4059 | 2.7660 | 3380 | 0.3659 |
|
| 222 |
+
| 0.4007 | 2.7823 | 3400 | 0.3659 |
|
| 223 |
+
| 0.4044 | 2.7987 | 3420 | 0.3663 |
|
| 224 |
+
| 0.4075 | 2.8151 | 3440 | 0.3658 |
|
| 225 |
+
| 0.4053 | 2.8314 | 3460 | 0.3660 |
|
| 226 |
+
| 0.4003 | 2.8478 | 3480 | 0.3664 |
|
| 227 |
+
| 0.4078 | 2.8642 | 3500 | 0.3662 |
|
| 228 |
+
| 0.4067 | 2.8805 | 3520 | 0.3661 |
|
| 229 |
+
| 0.4025 | 2.8969 | 3540 | 0.3660 |
|
| 230 |
+
| 0.4018 | 2.9133 | 3560 | 0.3658 |
|
| 231 |
+
| 0.4014 | 2.9296 | 3580 | 0.3661 |
|
| 232 |
+
| 0.4036 | 2.9460 | 3600 | 0.3661 |
|
| 233 |
+
| 0.4031 | 2.9624 | 3620 | 0.3657 |
|
| 234 |
+
| 0.4022 | 2.9787 | 3640 | 0.3660 |
|
| 235 |
+
| 0.4036 | 2.9951 | 3660 | 0.3662 |
|
| 236 |
|
| 237 |
|
| 238 |
### Framework versions
|
all_results.json
CHANGED
|
@@ -1,13 +1,13 @@
|
|
| 1 |
{
|
| 2 |
-
"epoch":
|
| 3 |
"eval_loss": 0.7127311825752258,
|
| 4 |
"eval_runtime": 27.5772,
|
| 5 |
"eval_samples_per_second": 607.423,
|
| 6 |
"eval_steps_per_second": 9.501,
|
| 7 |
"perplexity": 2.0395540531763667,
|
| 8 |
-
"total_flos":
|
| 9 |
-
"train_loss": 0.
|
| 10 |
-
"train_runtime":
|
| 11 |
-
"train_samples_per_second":
|
| 12 |
-
"train_steps_per_second": 0.
|
| 13 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"epoch": 3.0,
|
| 3 |
"eval_loss": 0.7127311825752258,
|
| 4 |
"eval_runtime": 27.5772,
|
| 5 |
"eval_samples_per_second": 607.423,
|
| 6 |
"eval_steps_per_second": 9.501,
|
| 7 |
"perplexity": 2.0395540531763667,
|
| 8 |
+
"total_flos": 1.5045077015543808e+16,
|
| 9 |
+
"train_loss": 0.5129519925375031,
|
| 10 |
+
"train_runtime": 8559.3775,
|
| 11 |
+
"train_samples_per_second": 27.411,
|
| 12 |
+
"train_steps_per_second": 0.428
|
| 13 |
}
|
train_results.json
CHANGED
|
@@ -1,8 +1,8 @@
|
|
| 1 |
{
|
| 2 |
-
"epoch":
|
| 3 |
-
"total_flos":
|
| 4 |
-
"train_loss": 0.
|
| 5 |
-
"train_runtime":
|
| 6 |
-
"train_samples_per_second":
|
| 7 |
-
"train_steps_per_second": 0.
|
| 8 |
}
|
|
|
|
| 1 |
{
|
| 2 |
+
"epoch": 3.0,
|
| 3 |
+
"total_flos": 1.5045077015543808e+16,
|
| 4 |
+
"train_loss": 0.5129519925375031,
|
| 5 |
+
"train_runtime": 8559.3775,
|
| 6 |
+
"train_samples_per_second": 27.411,
|
| 7 |
+
"train_steps_per_second": 0.428
|
| 8 |
}
|