commit 24f927d368e561affe8e497f8931ccdf8f3e034e Author: ModelHub XC Date: Sat May 30 10:09:19 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: Daewon0808/prm800k_llama_fulltune Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text 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100644 index 0000000..bb851ee --- /dev/null +++ b/README.md @@ -0,0 +1,849 @@ +--- +library_name: transformers +license: llama3.1 +base_model: meta-llama/Llama-3.1-8B-Instruct +tags: +- generated_from_trainer +model-index: +- name: prm800k_llama_fulltune + results: [] +--- + + + +# prm800k_llama_fulltune + +This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on an unknown dataset. +It achieves the following results on the evaluation set: +- Loss: 0.3303 +- Prm accuracy: 0.8491 +- Prm precision: 0.8851 +- Prm recall: 0.9277 +- Prm specificty: 0.5652 +- Prm npv: 0.6842 +- Prm f1: 0.9059 +- Prm f1 neg: 0.6190 +- Prm f1 auc: 0.7465 +- Prm f1 auc (fixed): 0.8876 + +## 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: 1.25e-06 +- train_batch_size: 1 +- eval_batch_size: 4 +- seed: 908932403 +- distributed_type: multi-GPU +- num_devices: 8 +- gradient_accumulation_steps: 16 +- total_train_batch_size: 128 +- total_eval_batch_size: 32 +- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments +- lr_scheduler_type: cosine +- lr_scheduler_warmup_ratio: 0.1 +- num_epochs: 1 + +### Training results + +| Training Loss | Epoch | Step | Validation Loss | Prm accuracy | Prm precision | Prm recall | Prm specificty | Prm npv | Prm f1 | Prm f1 neg | Prm f1 auc | Prm f1 auc (fixed) | +|:-------------:|:------:|:----:|:---------------:|:------------:|:-------------:|:----------:|:--------------:|:-------:|:------:|:----------:|:----------:|:------------------:| +| No log | 0 | 0 | 2.4183 | 0.2170 | 0.0 | 0.0 | 1.0 | 0.2170 | 0.0 | 0.3566 | 0.5 | 0.5406 | +| 2.402 | 0.0013 | 5 | 2.4242 | 0.2170 | 0.0 | 0.0 | 1.0 | 0.2170 | 0.0 | 0.3566 | 0.5 | 0.5388 | +| 2.3912 | 0.0026 | 10 | 2.4195 | 0.2170 | 0.0 | 0.0 | 1.0 | 0.2170 | 0.0 | 0.3566 | 0.5 | 0.5406 | +| 1.9656 | 0.0039 | 15 | 2.4064 | 0.2170 | 0.0 | 0.0 | 1.0 | 0.2170 | 0.0 | 0.3566 | 0.5 | 0.5430 | +| 2.4037 | 0.0052 | 20 | 2.3720 | 0.2170 | 0.0 | 0.0 | 1.0 | 0.2170 | 0.0 | 0.3566 | 0.5 | 0.5393 | +| 2.2229 | 0.0064 | 25 | 2.3099 | 0.2170 | 0.0 | 0.0 | 1.0 | 0.2170 | 0.0 | 0.3566 | 0.5 | 0.5435 | +| 2.0505 | 0.0077 | 30 | 2.2018 | 0.2170 | 0.0 | 0.0 | 1.0 | 0.2170 | 0.0 | 0.3566 | 0.5 | 0.5380 | +| 1.8872 | 0.0090 | 35 | 2.0501 | 0.2170 | 0.0 | 0.0 | 1.0 | 0.2170 | 0.0 | 0.3566 | 0.5 | 0.5430 | +| 1.5108 | 0.0103 | 40 | 1.6542 | 0.2264 | 0.6667 | 0.0241 | 0.9565 | 0.2136 | 0.0465 | 0.3492 | 0.4903 | 0.5443 | +| 1.441 | 0.0116 | 45 | 1.5106 | 0.2453 | 0.8 | 0.0482 | 0.9565 | 0.2178 | 0.0909 | 0.3548 | 0.5024 | 0.5437 | +| 0.9441 | 0.0129 | 50 | 0.9128 | 0.5377 | 0.7833 | 0.5663 | 0.4348 | 0.2174 | 0.6573 | 0.2899 | 0.5005 | 0.5464 | +| 0.7428 | 0.0142 | 55 | 0.8384 | 0.7170 | 0.7978 | 0.8554 | 0.2174 | 0.2941 | 0.8256 | 0.25 | 0.5364 | 0.5584 | +| 0.6675 | 0.0155 | 60 | 0.8218 | 0.6887 | 0.7907 | 0.8193 | 0.2174 | 0.25 | 0.8047 | 0.2326 | 0.5183 | 0.5812 | +| 0.7142 | 0.0167 | 65 | 0.8148 | 0.6887 | 0.7907 | 0.8193 | 0.2174 | 0.25 | 0.8047 | 0.2326 | 0.5183 | 0.6061 | +| 0.6989 | 0.0180 | 70 | 0.8088 | 0.6415 | 0.7778 | 0.7590 | 0.2174 | 0.2 | 0.7683 | 0.2083 | 0.4882 | 0.6341 | +| 0.5535 | 0.0193 | 75 | 0.7813 | 0.6792 | 0.7952 | 0.7952 | 0.2609 | 0.2609 | 0.7952 | 0.2609 | 0.5280 | 0.6624 | +| 0.7116 | 0.0206 | 80 | 0.7361 | 0.7264 | 0.8 | 0.8675 | 0.2174 | 0.3125 | 0.8324 | 0.2564 | 0.5424 | 0.7064 | +| 0.5911 | 0.0219 | 85 | 0.6919 | 0.7547 | 0.8276 | 0.8675 | 0.3478 | 0.4211 | 0.8471 | 0.3810 | 0.6076 | 0.7420 | +| 0.6673 | 0.0232 | 90 | 0.6848 | 0.7642 | 0.8625 | 0.8313 | 0.5217 | 0.4615 | 0.8466 | 0.4898 | 0.6765 | 0.7522 | +| 0.6204 | 0.0245 | 95 | 0.6879 | 0.7830 | 0.8571 | 0.8675 | 0.4783 | 0.5 | 0.8623 | 0.4889 | 0.6729 | 0.7452 | +| 0.5897 | 0.0258 | 100 | 0.6826 | 0.7642 | 0.8295 | 0.8795 | 0.3478 | 0.4444 | 0.8538 | 0.3902 | 0.6137 | 0.7373 | +| 0.573 | 0.0270 | 105 | 0.6591 | 0.7736 | 0.8391 | 0.8795 | 0.3913 | 0.4737 | 0.8588 | 0.4286 | 0.6354 | 0.7682 | +| 0.55 | 0.0283 | 110 | 0.6270 | 0.7925 | 0.8675 | 0.8675 | 0.5217 | 0.5217 | 0.8675 | 0.5217 | 0.6946 | 0.7988 | +| 0.555 | 0.0296 | 115 | 0.6040 | 0.8019 | 0.8780 | 0.8675 | 0.5652 | 0.5417 | 0.8727 | 0.5532 | 0.7163 | 0.8214 | +| 0.5097 | 0.0309 | 120 | 0.5911 | 0.8019 | 0.8605 | 0.8916 | 0.4783 | 0.55 | 0.8757 | 0.5116 | 0.6849 | 0.8206 | +| 0.5674 | 0.0322 | 125 | 0.6223 | 0.8113 | 0.8795 | 0.8795 | 0.5652 | 0.5652 | 0.8795 | 0.5652 | 0.7224 | 0.7829 | +| 0.4673 | 0.0335 | 130 | 0.6098 | 0.8019 | 0.8523 | 0.9036 | 0.4348 | 0.5556 | 0.8772 | 0.4878 | 0.6692 | 0.7666 | +| 0.5308 | 0.0348 | 135 | 0.5951 | 0.7925 | 0.8280 | 0.9277 | 0.3043 | 0.5385 | 0.875 | 0.3889 | 0.6160 | 0.7928 | +| 0.4568 | 0.0361 | 140 | 0.5634 | 0.7925 | 0.8675 | 0.8675 | 0.5217 | 0.5217 | 0.8675 | 0.5217 | 0.6946 | 0.8216 | +| 0.4926 | 0.0374 | 145 | 0.5657 | 0.8019 | 0.8605 | 0.8916 | 0.4783 | 0.55 | 0.8757 | 0.5116 | 0.6849 | 0.8054 | +| 0.4722 | 0.0386 | 150 | 0.5702 | 0.8019 | 0.8370 | 0.9277 | 0.3478 | 0.5714 | 0.88 | 0.4324 | 0.6378 | 0.8078 | +| 0.5006 | 0.0399 | 155 | 0.5614 | 0.7925 | 0.8861 | 0.8434 | 0.6087 | 0.5185 | 0.8642 | 0.56 | 0.7260 | 0.8235 | +| 0.5007 | 0.0412 | 160 | 0.5539 | 0.8113 | 0.8795 | 0.8795 | 0.5652 | 0.5652 | 0.8795 | 0.5652 | 0.7224 | 0.8193 | +| 0.4945 | 0.0425 | 165 | 0.5422 | 0.8208 | 0.8721 | 0.9036 | 0.5217 | 0.6 | 0.8876 | 0.5581 | 0.7127 | 0.8164 | +| 0.496 | 0.0438 | 170 | 0.5731 | 0.7830 | 0.8488 | 0.8795 | 0.4348 | 0.5 | 0.8639 | 0.4651 | 0.6572 | 0.7892 | +| 0.5088 | 0.0451 | 175 | 0.5772 | 0.7736 | 0.8391 | 0.8795 | 0.3913 | 0.4737 | 0.8588 | 0.4286 | 0.6354 | 0.7839 | +| 0.5202 | 0.0464 | 180 | 0.5567 | 0.8113 | 0.8539 | 0.9157 | 0.4348 | 0.5882 | 0.8837 | 0.5 | 0.6752 | 0.8146 | +| 0.4419 | 0.0477 | 185 | 0.5591 | 0.7642 | 0.8625 | 0.8313 | 0.5217 | 0.4615 | 0.8466 | 0.4898 | 0.6765 | 0.8222 | +| 0.5157 | 0.0489 | 190 | 0.5440 | 0.7925 | 0.8675 | 0.8675 | 0.5217 | 0.5217 | 0.8675 | 0.5217 | 0.6946 | 0.8130 | +| 0.4916 | 0.0502 | 195 | 0.5768 | 0.7453 | 0.8684 | 0.7952 | 0.5652 | 0.4333 | 0.8302 | 0.4906 | 0.6802 | 0.7808 | +| 0.4172 | 0.0515 | 200 | 0.5704 | 0.7830 | 0.8571 | 0.8675 | 0.4783 | 0.5 | 0.8623 | 0.4889 | 0.6729 | 0.7562 | +| 0.4321 | 0.0528 | 205 | 0.5444 | 0.7642 | 0.8537 | 0.8434 | 0.4783 | 0.4583 | 0.8485 | 0.4681 | 0.6608 | 0.7910 | +| 0.4774 | 0.0541 | 210 | 0.5514 | 0.7642 | 0.8718 | 0.8193 | 0.5652 | 0.4643 | 0.8447 | 0.5098 | 0.6922 | 0.8098 | +| 0.4112 | 0.0554 | 215 | 0.5246 | 0.7925 | 0.8588 | 0.8795 | 0.4783 | 0.5238 | 0.8690 | 0.5 | 0.6789 | 0.8125 | +| 0.4728 | 0.0567 | 220 | 0.6100 | 0.7358 | 0.8313 | 0.8313 | 0.3913 | 0.3913 | 0.8313 | 0.3913 | 0.6113 | 0.7483 | +| 0.4373 | 0.0580 | 225 | 0.6735 | 0.6415 | 0.8358 | 0.6747 | 0.5217 | 0.3077 | 0.7467 | 0.3871 | 0.5982 | 0.7386 | +| 0.6191 | 0.0592 | 230 | 0.5636 | 0.7736 | 0.8242 | 0.9036 | 0.3043 | 0.4667 | 0.8621 | 0.3684 | 0.6040 | 0.8091 | +| 0.4375 | 0.0605 | 235 | 0.5414 | 0.7642 | 0.8919 | 0.7952 | 0.6522 | 0.4688 | 0.8408 | 0.5455 | 0.7237 | 0.8376 | +| 0.466 | 0.0618 | 240 | 0.5359 | 0.7642 | 0.8919 | 0.7952 | 0.6522 | 0.4688 | 0.8408 | 0.5455 | 0.7237 | 0.8274 | +| 0.4541 | 0.0631 | 245 | 0.5321 | 0.7736 | 0.8831 | 0.8193 | 0.6087 | 0.4828 | 0.85 | 0.5385 | 0.7140 | 0.8180 | +| 0.4835 | 0.0644 | 250 | 0.5274 | 0.7736 | 0.8554 | 0.8554 | 0.4783 | 0.4783 | 0.8554 | 0.4783 | 0.6668 | 0.8332 | +| 0.5002 | 0.0657 | 255 | 0.5161 | 0.7830 | 0.8659 | 0.8554 | 0.5217 | 0.5 | 0.8606 | 0.5106 | 0.6886 | 0.8324 | +| 0.5411 | 0.0670 | 260 | 0.5746 | 0.7453 | 0.9 | 0.7590 | 0.6957 | 0.4444 | 0.8235 | 0.5424 | 0.7273 | 0.8093 | +| 0.4657 | 0.0683 | 265 | 0.5168 | 0.7736 | 0.8734 | 0.8313 | 0.5652 | 0.4815 | 0.8519 | 0.52 | 0.6983 | 0.8224 | +| 0.4689 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0.8447 | 0.5098 | 0.6922 | 0.8041 | +| 0.3957 | 0.0811 | 315 | 0.5046 | 0.7170 | 0.8732 | 0.7470 | 0.6087 | 0.4 | 0.8052 | 0.4828 | 0.6778 | 0.8007 | +| 0.4593 | 0.0824 | 320 | 0.4987 | 0.7264 | 0.8649 | 0.7711 | 0.5652 | 0.4062 | 0.8153 | 0.4727 | 0.6682 | 0.7952 | +| 0.4228 | 0.0837 | 325 | 0.5015 | 0.7358 | 0.8767 | 0.7711 | 0.6087 | 0.4242 | 0.8205 | 0.5 | 0.6899 | 0.7970 | +| 0.4059 | 0.0850 | 330 | 0.4907 | 0.7642 | 0.8537 | 0.8434 | 0.4783 | 0.4583 | 0.8485 | 0.4681 | 0.6608 | 0.8049 | +| 0.4831 | 0.0863 | 335 | 0.5183 | 0.7264 | 0.8857 | 0.7470 | 0.6522 | 0.4167 | 0.8105 | 0.5085 | 0.6996 | 0.8017 | +| 0.5011 | 0.0876 | 340 | 0.4636 | 0.7830 | 0.8571 | 0.8675 | 0.4783 | 0.5 | 0.8623 | 0.4889 | 0.6729 | 0.8274 | +| 0.4312 | 0.0889 | 345 | 0.4622 | 0.7642 | 0.8919 | 0.7952 | 0.6522 | 0.4688 | 0.8408 | 0.5455 | 0.7237 | 0.8279 | +| 0.4772 | 0.0902 | 350 | 0.4842 | 0.7642 | 0.8452 | 0.8554 | 0.4348 | 0.4545 | 0.8503 | 0.4444 | 0.6451 | 0.8015 | +| 0.3951 | 0.0914 | 355 | 0.4450 | 0.7547 | 0.88 | 0.7952 | 0.6087 | 0.4516 | 0.8354 | 0.5185 | 0.7019 | 0.8235 | +| 0.3948 | 0.0927 | 360 | 0.4456 | 0.7547 | 0.88 | 0.7952 | 0.6087 | 0.4516 | 0.8354 | 0.5185 | 0.7019 | 0.8156 | +| 0.5416 | 0.0940 | 365 | 0.5189 | 0.7170 | 0.8533 | 0.7711 | 0.5217 | 0.3871 | 0.8101 | 0.4444 | 0.6464 | 0.7624 | +| 0.4922 | 0.0953 | 370 | 0.4846 | 0.7264 | 0.8462 | 0.7952 | 0.4783 | 0.3929 | 0.8199 | 0.4314 | 0.6367 | 0.7844 | +| 0.4291 | 0.0966 | 375 | 0.4914 | 0.7170 | 0.8442 | 0.7831 | 0.4783 | 0.3793 | 0.8125 | 0.4231 | 0.6307 | 0.7779 | +| 0.4144 | 0.0979 | 380 | 0.4846 | 0.7170 | 0.8354 | 0.7952 | 0.4348 | 0.3704 | 0.8148 | 0.4 | 0.6150 | 0.7844 | +| 0.4247 | 0.0992 | 385 | 0.5149 | 0.7170 | 0.8955 | 0.7229 | 0.6957 | 0.4103 | 0.8 | 0.5161 | 0.7093 | 0.7742 | +| 0.4139 | 0.1005 | 390 | 0.4781 | 0.7547 | 0.8434 | 0.8434 | 0.4348 | 0.4348 | 0.8434 | 0.4348 | 0.6391 | 0.7876 | +| 0.4385 | 0.1018 | 395 | 0.4992 | 0.6981 | 0.84 | 0.7590 | 0.4783 | 0.3548 | 0.7975 | 0.4074 | 0.6186 | 0.7656 | +| 0.3952 | 0.1030 | 400 | 0.5110 | 0.7264 | 0.8293 | 0.8193 | 0.3913 | 0.375 | 0.8242 | 0.3830 | 0.6053 | 0.7596 | +| 0.4054 | 0.1043 | 405 | 0.4934 | 0.7264 | 0.8553 | 0.7831 | 0.5217 | 0.4 | 0.8176 | 0.4528 | 0.6524 | 0.7768 | +| 0.394 | 0.1056 | 410 | 0.4560 | 0.7453 | 0.8415 | 0.8313 | 0.4348 | 0.4167 | 0.8364 | 0.4255 | 0.6331 | 0.8004 | +| 0.4103 | 0.1069 | 415 | 0.5106 | 0.7170 | 0.9344 | 0.6867 | 0.8261 | 0.4222 | 0.7917 | 0.5588 | 0.7564 | 0.8070 | +| 0.5192 | 0.1082 | 420 | 0.4449 | 0.7358 | 0.8313 | 0.8313 | 0.3913 | 0.3913 | 0.8313 | 0.3913 | 0.6113 | 0.8159 | +| 0.3841 | 0.1095 | 425 | 0.4485 | 0.7642 | 0.8718 | 0.8193 | 0.5652 | 0.4643 | 0.8447 | 0.5098 | 0.6922 | 0.8229 | +| 0.4419 | 0.1108 | 430 | 0.4440 | 0.7642 | 0.8718 | 0.8193 | 0.5652 | 0.4643 | 0.8447 | 0.5098 | 0.6922 | 0.8313 | +| 0.3641 | 0.1121 | 435 | 0.4380 | 0.7736 | 0.8642 | 0.8434 | 0.5217 | 0.48 | 0.8537 | 0.5 | 0.6826 | 0.8447 | +| 0.3839 | 0.1133 | 440 | 0.4343 | 0.7642 | 0.8625 | 0.8313 | 0.5217 | 0.4615 | 0.8466 | 0.4898 | 0.6765 | 0.8489 | +| 0.3835 | 0.1146 | 445 | 0.4342 | 0.7547 | 0.88 | 0.7952 | 0.6087 | 0.4516 | 0.8354 | 0.5185 | 0.7019 | 0.8431 | +| 0.4345 | 0.1159 | 450 | 0.4376 | 0.7736 | 0.8642 | 0.8434 | 0.5217 | 0.48 | 0.8537 | 0.5 | 0.6826 | 0.8421 | +| 0.4758 | 0.1172 | 455 | 0.4445 | 0.7547 | 0.8608 | 0.8193 | 0.5217 | 0.4444 | 0.8395 | 0.48 | 0.6705 | 0.8394 | +| 0.3932 | 0.1185 | 460 | 0.4644 | 0.7642 | 0.9028 | 0.7831 | 0.6957 | 0.4706 | 0.8387 | 0.5614 | 0.7394 | 0.8279 | +| 0.3594 | 0.1198 | 465 | 0.4948 | 0.7642 | 0.8625 | 0.8313 | 0.5217 | 0.4615 | 0.8466 | 0.4898 | 0.6765 | 0.8096 | +| 0.4264 | 0.1211 | 470 | 0.4865 | 0.7736 | 0.9041 | 0.7952 | 0.6957 | 0.4848 | 0.8462 | 0.5714 | 0.7454 | 0.8101 | +| 0.3738 | 0.1224 | 475 | 0.4310 | 0.8019 | 0.9079 | 0.8313 | 0.6957 | 0.5333 | 0.8679 | 0.6038 | 0.7635 | 0.8237 | +| 0.421 | 0.1236 | 480 | 0.4328 | 0.7925 | 0.8675 | 0.8675 | 0.5217 | 0.5217 | 0.8675 | 0.5217 | 0.6946 | 0.8263 | +| 0.4172 | 0.1249 | 485 | 0.4902 | 0.7264 | 0.9091 | 0.7229 | 0.7391 | 0.425 | 0.8054 | 0.5397 | 0.7310 | 0.8206 | +| 0.4219 | 0.1262 | 490 | 0.4261 | 0.7925 | 0.8861 | 0.8434 | 0.6087 | 0.5185 | 0.8642 | 0.56 | 0.7260 | 0.8193 | +| 0.3889 | 0.1275 | 495 | 0.4140 | 0.8019 | 0.8780 | 0.8675 | 0.5652 | 0.5417 | 0.8727 | 0.5532 | 0.7163 | 0.8258 | +| 0.409 | 0.1288 | 500 | 0.4281 | 0.7736 | 0.9275 | 0.7711 | 0.7826 | 0.4865 | 0.8421 | 0.6 | 0.7768 | 0.8379 | +| 0.3302 | 0.1301 | 505 | 0.3853 | 0.8208 | 0.8810 | 0.8916 | 0.5652 | 0.5909 | 0.8862 | 0.5778 | 0.7284 | 0.8410 | +| 0.3629 | 0.1314 | 510 | 0.3956 | 0.8113 | 0.9091 | 0.8434 | 0.6957 | 0.5517 | 0.875 | 0.6154 | 0.7695 | 0.8415 | +| 0.3636 | 0.1327 | 515 | 0.4095 | 0.8113 | 0.9091 | 0.8434 | 0.6957 | 0.5517 | 0.875 | 0.6154 | 0.7695 | 0.8444 | +| 0.3963 | 0.1339 | 520 | 0.3869 | 0.8208 | 0.9103 | 0.8554 | 0.6957 | 0.5714 | 0.8820 | 0.6275 | 0.7755 | 0.8489 | +| 0.4552 | 0.1352 | 525 | 0.3953 | 0.7830 | 0.8659 | 0.8554 | 0.5217 | 0.5 | 0.8606 | 0.5106 | 0.6886 | 0.8601 | +| 0.3152 | 0.1365 | 530 | 0.3675 | 0.7736 | 0.8642 | 0.8434 | 0.5217 | 0.48 | 0.8537 | 0.5 | 0.6826 | 0.8782 | +| 0.3878 | 0.1378 | 535 | 0.3752 | 0.7830 | 0.9167 | 0.7952 | 0.7391 | 0.5 | 0.8516 | 0.5965 | 0.7672 | 0.8795 | +| 0.3638 | 0.1391 | 540 | 0.3752 | 0.7736 | 0.8933 | 0.8072 | 0.6522 | 0.4839 | 0.8481 | 0.5556 | 0.7297 | 0.8654 | +| 0.3646 | 0.1404 | 545 | 0.3893 | 0.7736 | 0.8642 | 0.8434 | 0.5217 | 0.48 | 0.8537 | 0.5 | 0.6826 | 0.8541 | +| 0.4395 | 0.1417 | 550 | 0.4148 | 0.7547 | 0.9130 | 0.7590 | 0.7391 | 0.4595 | 0.8289 | 0.5667 | 0.7491 | 0.8290 | +| 0.3965 | 0.1430 | 555 | 0.3980 | 0.7642 | 0.8625 | 0.8313 | 0.5217 | 0.4615 | 0.8466 | 0.4898 | 0.6765 | 0.8347 | +| 0.3798 | 0.1443 | 560 | 0.4434 | 0.7830 | 0.9286 | 0.7831 | 0.7826 | 0.5 | 0.8497 | 0.6102 | 0.7829 | 0.8235 | +| 0.4103 | 0.1455 | 565 | 0.4262 | 0.7736 | 0.8471 | 0.8675 | 0.4348 | 0.4762 | 0.8571 | 0.4545 | 0.6511 | 0.8339 | +| 0.3741 | 0.1468 | 570 | 0.3911 | 0.7925 | 0.8765 | 0.8554 | 0.5652 | 0.52 | 0.8659 | 0.5417 | 0.7103 | 0.8512 | +| 0.4197 | 0.1481 | 575 | 0.3902 | 0.7925 | 0.8765 | 0.8554 | 0.5652 | 0.52 | 0.8659 | 0.5417 | 0.7103 | 0.8444 | +| 0.4768 | 0.1494 | 580 | 0.4010 | 0.7736 | 0.9041 | 0.7952 | 0.6957 | 0.4848 | 0.8462 | 0.5714 | 0.7454 | 0.8392 | +| 0.3457 | 0.1507 | 585 | 0.4176 | 0.7925 | 0.8588 | 0.8795 | 0.4783 | 0.5238 | 0.8690 | 0.5 | 0.6789 | 0.8387 | +| 0.437 | 0.1520 | 590 | 0.4229 | 0.7642 | 0.8718 | 0.8193 | 0.5652 | 0.4643 | 0.8447 | 0.5098 | 0.6922 | 0.8318 | +| 0.3508 | 0.1533 | 595 | 0.3975 | 0.8208 | 0.9 | 0.8675 | 0.6522 | 0.5769 | 0.8834 | 0.6122 | 0.7598 | 0.8444 | +| 0.3985 | 0.1546 | 600 | 0.3766 | 0.8208 | 0.9103 | 0.8554 | 0.6957 | 0.5714 | 0.8820 | 0.6275 | 0.7755 | 0.8580 | +| 0.3831 | 0.1558 | 605 | 0.3778 | 0.8302 | 0.9114 | 0.8675 | 0.6957 | 0.5926 | 0.8889 | 0.64 | 0.7816 | 0.8594 | +| 0.4736 | 0.1571 | 610 | 0.3785 | 0.8208 | 0.8810 | 0.8916 | 0.5652 | 0.5909 | 0.8862 | 0.5778 | 0.7284 | 0.8625 | +| 0.5239 | 0.1584 | 615 | 0.3708 | 0.8208 | 0.9 | 0.8675 | 0.6522 | 0.5769 | 0.8834 | 0.6122 | 0.7598 | 0.8719 | +| 0.4432 | 0.1597 | 620 | 0.3630 | 0.8113 | 0.9091 | 0.8434 | 0.6957 | 0.5517 | 0.875 | 0.6154 | 0.7695 | 0.8777 | +| 0.3978 | 0.1610 | 625 | 0.4023 | 0.8208 | 0.8556 | 0.9277 | 0.4348 | 0.625 | 0.8902 | 0.5128 | 0.6812 | 0.8774 | +| 0.4202 | 0.1623 | 630 | 0.3852 | 0.8113 | 0.92 | 0.8313 | 0.7391 | 0.5484 | 0.8734 | 0.6296 | 0.7852 | 0.8724 | +| 0.4682 | 0.1636 | 635 | 0.4054 | 0.7925 | 0.8675 | 0.8675 | 0.5217 | 0.5217 | 0.8675 | 0.5217 | 0.6946 | 0.8651 | +| 0.4159 | 0.1649 | 640 | 0.4164 | 0.7830 | 0.9054 | 0.8072 | 0.6957 | 0.5 | 0.8535 | 0.5818 | 0.7514 | 0.8596 | +| 0.3716 | 0.1661 | 645 | 0.4086 | 0.7642 | 0.8718 | 0.8193 | 0.5652 | 0.4643 | 0.8447 | 0.5098 | 0.6922 | 0.8573 | +| 0.4299 | 0.1674 | 650 | 0.4043 | 0.7830 | 0.8846 | 0.8313 | 0.6087 | 0.5 | 0.8571 | 0.5490 | 0.7200 | 0.8588 | +| 0.3573 | 0.1687 | 655 | 0.4061 | 0.7830 | 0.8846 | 0.8313 | 0.6087 | 0.5 | 0.8571 | 0.5490 | 0.7200 | 0.8575 | +| 0.5262 | 0.1700 | 660 | 0.3973 | 0.8113 | 0.8795 | 0.8795 | 0.5652 | 0.5652 | 0.8795 | 0.5652 | 0.7224 | 0.8669 | +| 0.3865 | 0.1713 | 665 | 0.3912 | 0.7547 | 0.9014 | 0.7711 | 0.6957 | 0.4571 | 0.8312 | 0.5517 | 0.7334 | 0.8790 | +| 0.4587 | 0.1726 | 670 | 0.3733 | 0.7830 | 0.8947 | 0.8193 | 0.6522 | 0.5 | 0.8553 | 0.5660 | 0.7357 | 0.8793 | +| 0.3987 | 0.1739 | 675 | 0.3694 | 0.8113 | 0.8795 | 0.8795 | 0.5652 | 0.5652 | 0.8795 | 0.5652 | 0.7224 | 0.8811 | +| 0.4506 | 0.1752 | 680 | 0.3567 | 0.7830 | 0.9167 | 0.7952 | 0.7391 | 0.5 | 0.8516 | 0.5965 | 0.7672 | 0.8764 | +| 0.3626 | 0.1765 | 685 | 0.3626 | 0.7736 | 0.9041 | 0.7952 | 0.6957 | 0.4848 | 0.8462 | 0.5714 | 0.7454 | 0.8732 | +| 0.4138 | 0.1777 | 690 | 0.3711 | 0.7736 | 0.9041 | 0.7952 | 0.6957 | 0.4848 | 0.8462 | 0.5714 | 0.7454 | 0.8738 | +| 0.3349 | 0.1790 | 695 | 0.3625 | 0.7830 | 0.8947 | 0.8193 | 0.6522 | 0.5 | 0.8553 | 0.5660 | 0.7357 | 0.8779 | +| 0.3786 | 0.1803 | 700 | 0.3433 | 0.7925 | 0.8961 | 0.8313 | 0.6522 | 0.5172 | 0.8625 | 0.5769 | 0.7417 | 0.8884 | +| 0.3878 | 0.1816 | 705 | 0.3373 | 0.7925 | 0.9067 | 0.8193 | 0.6957 | 0.5161 | 0.8608 | 0.5926 | 0.7575 | 0.8892 | +| 0.3612 | 0.1829 | 710 | 0.3591 | 0.7830 | 0.9054 | 0.8072 | 0.6957 | 0.5 | 0.8535 | 0.5818 | 0.7514 | 0.8798 | +| 0.4144 | 0.1842 | 715 | 0.3855 | 0.7830 | 0.9167 | 0.7952 | 0.7391 | 0.5 | 0.8516 | 0.5965 | 0.7672 | 0.8662 | +| 0.3835 | 0.1855 | 720 | 0.3934 | 0.8019 | 0.8605 | 0.8916 | 0.4783 | 0.55 | 0.8757 | 0.5116 | 0.6849 | 0.8583 | +| 0.3547 | 0.1868 | 725 | 0.4013 | 0.7830 | 0.8659 | 0.8554 | 0.5217 | 0.5 | 0.8606 | 0.5106 | 0.6886 | 0.8507 | +| 0.3996 | 0.1880 | 730 | 0.3811 | 0.7830 | 0.8846 | 0.8313 | 0.6087 | 0.5 | 0.8571 | 0.5490 | 0.7200 | 0.8583 | +| 0.4807 | 0.1893 | 735 | 0.3792 | 0.8302 | 0.8916 | 0.8916 | 0.6087 | 0.6087 | 0.8916 | 0.6087 | 0.7501 | 0.8656 | +| 0.4267 | 0.1906 | 740 | 0.3750 | 0.7830 | 0.8947 | 0.8193 | 0.6522 | 0.5 | 0.8553 | 0.5660 | 0.7357 | 0.8664 | +| 0.4233 | 0.1919 | 745 | 0.3555 | 0.8019 | 0.8974 | 0.8434 | 0.6522 | 0.5357 | 0.8696 | 0.5882 | 0.7478 | 0.8740 | +| 0.3267 | 0.1932 | 750 | 0.3835 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8772 | +| 0.4284 | 0.1945 | 755 | 0.3775 | 0.7925 | 0.8861 | 0.8434 | 0.6087 | 0.5185 | 0.8642 | 0.56 | 0.7260 | 0.8628 | +| 0.3603 | 0.1958 | 760 | 0.3964 | 0.7925 | 0.8861 | 0.8434 | 0.6087 | 0.5185 | 0.8642 | 0.56 | 0.7260 | 0.8465 | +| 0.4747 | 0.1971 | 765 | 0.4147 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8394 | +| 0.443 | 0.1983 | 770 | 0.3903 | 0.8396 | 0.9024 | 0.8916 | 0.6522 | 0.625 | 0.8970 | 0.6383 | 0.7719 | 0.8523 | +| 0.4344 | 0.1996 | 775 | 0.3763 | 0.8208 | 0.9 | 0.8675 | 0.6522 | 0.5769 | 0.8834 | 0.6122 | 0.7598 | 0.8609 | +| 0.4224 | 0.2009 | 780 | 0.3735 | 0.8208 | 0.9 | 0.8675 | 0.6522 | 0.5769 | 0.8834 | 0.6122 | 0.7598 | 0.8638 | +| 0.467 | 0.2022 | 785 | 0.3662 | 0.7925 | 0.8961 | 0.8313 | 0.6522 | 0.5172 | 0.8625 | 0.5769 | 0.7417 | 0.8669 | +| 0.4358 | 0.2035 | 790 | 0.3741 | 0.8208 | 0.8902 | 0.8795 | 0.6087 | 0.5833 | 0.8848 | 0.5957 | 0.7441 | 0.8601 | +| 0.3727 | 0.2048 | 795 | 0.3735 | 0.8208 | 0.9 | 0.8675 | 0.6522 | 0.5769 | 0.8834 | 0.6122 | 0.7598 | 0.8591 | +| 0.3995 | 0.2061 | 800 | 0.3632 | 0.8396 | 0.9024 | 0.8916 | 0.6522 | 0.625 | 0.8970 | 0.6383 | 0.7719 | 0.8675 | +| 0.4056 | 0.2074 | 805 | 0.3655 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8656 | +| 0.3616 | 0.2087 | 810 | 0.3527 | 0.8019 | 0.9079 | 0.8313 | 0.6957 | 0.5333 | 0.8679 | 0.6038 | 0.7635 | 0.8649 | +| 0.4304 | 0.2099 | 815 | 0.3608 | 0.8491 | 0.8941 | 0.9157 | 0.6087 | 0.6667 | 0.9048 | 0.6364 | 0.7622 | 0.8594 | +| 0.4115 | 0.2112 | 820 | 0.3769 | 0.8302 | 0.9012 | 0.8795 | 0.6522 | 0.6 | 0.8902 | 0.625 | 0.7658 | 0.8423 | +| 0.3726 | 0.2125 | 825 | 0.3797 | 0.8396 | 0.9024 | 0.8916 | 0.6522 | 0.625 | 0.8970 | 0.6383 | 0.7719 | 0.8355 | +| 0.3459 | 0.2138 | 830 | 0.3534 | 0.8302 | 0.9114 | 0.8675 | 0.6957 | 0.5926 | 0.8889 | 0.64 | 0.7816 | 0.8559 | +| 0.4927 | 0.2151 | 835 | 0.3673 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8641 | +| 0.4451 | 0.2164 | 840 | 0.3523 | 0.8302 | 0.9114 | 0.8675 | 0.6957 | 0.5926 | 0.8889 | 0.64 | 0.7816 | 0.8745 | +| 0.3951 | 0.2177 | 845 | 0.3600 | 0.8491 | 0.9036 | 0.9036 | 0.6522 | 0.6522 | 0.9036 | 0.6522 | 0.7779 | 0.8753 | +| 0.4315 | 0.2190 | 850 | 0.3443 | 0.8302 | 0.9012 | 0.8795 | 0.6522 | 0.6 | 0.8902 | 0.625 | 0.7658 | 0.8772 | +| 0.3963 | 0.2202 | 855 | 0.3599 | 0.8302 | 0.9012 | 0.8795 | 0.6522 | 0.6 | 0.8902 | 0.625 | 0.7658 | 0.8622 | +| 0.313 | 0.2215 | 860 | 0.3728 | 0.8208 | 0.9 | 0.8675 | 0.6522 | 0.5769 | 0.8834 | 0.6122 | 0.7598 | 0.8463 | +| 0.2756 | 0.2228 | 865 | 0.3889 | 0.8302 | 0.9012 | 0.8795 | 0.6522 | 0.6 | 0.8902 | 0.625 | 0.7658 | 0.8410 | +| 0.3261 | 0.2241 | 870 | 0.3876 | 0.8396 | 0.9024 | 0.8916 | 0.6522 | 0.625 | 0.8970 | 0.6383 | 0.7719 | 0.8478 | +| 0.4006 | 0.2254 | 875 | 0.3630 | 0.8208 | 0.9 | 0.8675 | 0.6522 | 0.5769 | 0.8834 | 0.6122 | 0.7598 | 0.8473 | +| 0.362 | 0.2267 | 880 | 0.3566 | 0.8208 | 0.9 | 0.8675 | 0.6522 | 0.5769 | 0.8834 | 0.6122 | 0.7598 | 0.8520 | +| 0.3969 | 0.2280 | 885 | 0.3744 | 0.8868 | 0.9080 | 0.9518 | 0.6522 | 0.7895 | 0.9294 | 0.7143 | 0.8020 | 0.8567 | +| 0.3845 | 0.2293 | 890 | 0.3542 | 0.8491 | 0.9036 | 0.9036 | 0.6522 | 0.6522 | 0.9036 | 0.6522 | 0.7779 | 0.8633 | +| 0.4255 | 0.2305 | 895 | 0.3500 | 0.8208 | 0.9 | 0.8675 | 0.6522 | 0.5769 | 0.8834 | 0.6122 | 0.7598 | 0.8693 | +| 0.383 | 0.2318 | 900 | 0.4012 | 0.8491 | 0.8681 | 0.9518 | 0.4783 | 0.7333 | 0.9080 | 0.5789 | 0.7150 | 0.8638 | +| 0.516 | 0.2331 | 905 | 0.3793 | 0.8585 | 0.8953 | 0.9277 | 0.6087 | 0.7 | 0.9112 | 0.6512 | 0.7682 | 0.8552 | +| 0.3996 | 0.2344 | 910 | 0.3793 | 0.8208 | 0.9 | 0.8675 | 0.6522 | 0.5769 | 0.8834 | 0.6122 | 0.7598 | 0.8494 | +| 0.427 | 0.2357 | 915 | 0.4075 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8491 | +| 0.4367 | 0.2370 | 920 | 0.3717 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8533 | +| 0.3904 | 0.2383 | 925 | 0.3507 | 0.7925 | 0.9067 | 0.8193 | 0.6957 | 0.5161 | 0.8608 | 0.5926 | 0.7575 | 0.8633 | +| 0.3712 | 0.2396 | 930 | 0.3514 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8824 | +| 0.4077 | 0.2409 | 935 | 0.3493 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8766 | +| 0.3887 | 0.2421 | 940 | 0.3598 | 0.8302 | 0.9012 | 0.8795 | 0.6522 | 0.6 | 0.8902 | 0.625 | 0.7658 | 0.8630 | +| 0.3529 | 0.2434 | 945 | 0.3646 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8617 | +| 0.4239 | 0.2447 | 950 | 0.3537 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8649 | +| 0.4201 | 0.2460 | 955 | 0.3404 | 0.8208 | 0.9103 | 0.8554 | 0.6957 | 0.5714 | 0.8820 | 0.6275 | 0.7755 | 0.8709 | +| 0.3675 | 0.2473 | 960 | 0.3447 | 0.8491 | 0.8941 | 0.9157 | 0.6087 | 0.6667 | 0.9048 | 0.6364 | 0.7622 | 0.8719 | +| 0.3732 | 0.2486 | 965 | 0.3282 | 0.8302 | 0.9012 | 0.8795 | 0.6522 | 0.6 | 0.8902 | 0.625 | 0.7658 | 0.8740 | +| 0.4331 | 0.2499 | 970 | 0.3213 | 0.8208 | 0.9103 | 0.8554 | 0.6957 | 0.5714 | 0.8820 | 0.6275 | 0.7755 | 0.8811 | +| 0.3637 | 0.2512 | 975 | 0.3276 | 0.8302 | 0.8916 | 0.8916 | 0.6087 | 0.6087 | 0.8916 | 0.6087 | 0.7501 | 0.8795 | +| 0.4258 | 0.2524 | 980 | 0.3447 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8811 | +| 0.3663 | 0.2537 | 985 | 0.3225 | 0.8302 | 0.9114 | 0.8675 | 0.6957 | 0.5926 | 0.8889 | 0.64 | 0.7816 | 0.8840 | +| 0.363 | 0.2550 | 990 | 0.3076 | 0.8208 | 0.9 | 0.8675 | 0.6522 | 0.5769 | 0.8834 | 0.6122 | 0.7598 | 0.8939 | +| 0.3337 | 0.2563 | 995 | 0.3005 | 0.8302 | 0.9114 | 0.8675 | 0.6957 | 0.5926 | 0.8889 | 0.64 | 0.7816 | 0.8997 | +| 0.3968 | 0.2576 | 1000 | 0.3121 | 0.8208 | 0.8810 | 0.8916 | 0.5652 | 0.5909 | 0.8862 | 0.5778 | 0.7284 | 0.8989 | +| 0.4 | 0.2589 | 1005 | 0.3167 | 0.8302 | 0.9012 | 0.8795 | 0.6522 | 0.6 | 0.8902 | 0.625 | 0.7658 | 0.8897 | +| 0.4106 | 0.2602 | 1010 | 0.3327 | 0.8302 | 0.9221 | 0.8554 | 0.7391 | 0.5862 | 0.8875 | 0.6538 | 0.7973 | 0.8774 | +| 0.4033 | 0.2615 | 1015 | 0.3355 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8855 | +| 0.3694 | 0.2627 | 1020 | 0.3324 | 0.8208 | 0.9211 | 0.8434 | 0.7391 | 0.5667 | 0.8805 | 0.6415 | 0.7913 | 0.8696 | +| 0.4224 | 0.2640 | 1025 | 0.3273 | 0.8302 | 0.9012 | 0.8795 | 0.6522 | 0.6 | 0.8902 | 0.625 | 0.7658 | 0.8732 | +| 0.4713 | 0.2653 | 1030 | 0.3530 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8824 | +| 0.3606 | 0.2666 | 1035 | 0.3391 | 0.8208 | 0.8810 | 0.8916 | 0.5652 | 0.5909 | 0.8862 | 0.5778 | 0.7284 | 0.8845 | +| 0.3667 | 0.2679 | 1040 | 0.3433 | 0.8208 | 0.9103 | 0.8554 | 0.6957 | 0.5714 | 0.8820 | 0.6275 | 0.7755 | 0.8837 | +| 0.371 | 0.2692 | 1045 | 0.3400 | 0.8302 | 0.9012 | 0.8795 | 0.6522 | 0.6 | 0.8902 | 0.625 | 0.7658 | 0.8827 | +| 0.3964 | 0.2705 | 1050 | 0.3801 | 0.8585 | 0.8778 | 0.9518 | 0.5217 | 0.75 | 0.9133 | 0.6154 | 0.7368 | 0.8837 | +| 0.4229 | 0.2718 | 1055 | 0.3490 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8832 | +| 0.4032 | 0.2731 | 1060 | 0.3481 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8827 | +| 0.3131 | 0.2743 | 1065 | 0.3455 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8819 | +| 0.358 | 0.2756 | 1070 | 0.3494 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8795 | +| 0.2622 | 0.2769 | 1075 | 0.3559 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8730 | +| 0.4125 | 0.2782 | 1080 | 0.3629 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8688 | +| 0.4288 | 0.2795 | 1085 | 0.3571 | 0.8208 | 0.9 | 0.8675 | 0.6522 | 0.5769 | 0.8834 | 0.6122 | 0.7598 | 0.8596 | +| 0.3785 | 0.2808 | 1090 | 0.3627 | 0.8113 | 0.8795 | 0.8795 | 0.5652 | 0.5652 | 0.8795 | 0.5652 | 0.7224 | 0.8641 | +| 0.3495 | 0.2821 | 1095 | 0.3872 | 0.8113 | 0.8621 | 0.9036 | 0.4783 | 0.5789 | 0.8824 | 0.5238 | 0.6909 | 0.8601 | +| 0.4395 | 0.2834 | 1100 | 0.3830 | 0.7830 | 0.8846 | 0.8313 | 0.6087 | 0.5 | 0.8571 | 0.5490 | 0.7200 | 0.8405 | +| 0.3134 | 0.2846 | 1105 | 0.4069 | 0.7642 | 0.8537 | 0.8434 | 0.4783 | 0.4583 | 0.8485 | 0.4681 | 0.6608 | 0.8318 | +| 0.4935 | 0.2859 | 1110 | 0.4522 | 0.8208 | 0.8478 | 0.9398 | 0.3913 | 0.6429 | 0.8914 | 0.4865 | 0.6655 | 0.8316 | +| 0.413 | 0.2872 | 1115 | 0.4207 | 0.7830 | 0.8571 | 0.8675 | 0.4783 | 0.5 | 0.8623 | 0.4889 | 0.6729 | 0.8261 | +| 0.3103 | 0.2885 | 1120 | 0.4321 | 0.8113 | 0.8462 | 0.9277 | 0.3913 | 0.6 | 0.8851 | 0.4737 | 0.6595 | 0.8376 | +| 0.3434 | 0.2898 | 1125 | 0.4030 | 0.7925 | 0.8961 | 0.8313 | 0.6522 | 0.5172 | 0.8625 | 0.5769 | 0.7417 | 0.8368 | +| 0.4057 | 0.2911 | 1130 | 0.3996 | 0.8019 | 0.8974 | 0.8434 | 0.6522 | 0.5357 | 0.8696 | 0.5882 | 0.7478 | 0.8300 | +| 0.3039 | 0.2924 | 1135 | 0.4296 | 0.8019 | 0.8605 | 0.8916 | 0.4783 | 0.55 | 0.8757 | 0.5116 | 0.6849 | 0.8298 | +| 0.4658 | 0.2937 | 1140 | 0.4079 | 0.8208 | 0.9 | 0.8675 | 0.6522 | 0.5769 | 0.8834 | 0.6122 | 0.7598 | 0.8245 | +| 0.3508 | 0.2949 | 1145 | 0.4047 | 0.8019 | 0.8974 | 0.8434 | 0.6522 | 0.5357 | 0.8696 | 0.5882 | 0.7478 | 0.8169 | +| 0.3735 | 0.2962 | 1150 | 0.4350 | 0.8208 | 0.8721 | 0.9036 | 0.5217 | 0.6 | 0.8876 | 0.5581 | 0.7127 | 0.8229 | +| 0.3914 | 0.2975 | 1155 | 0.4169 | 0.7925 | 0.8861 | 0.8434 | 0.6087 | 0.5185 | 0.8642 | 0.56 | 0.7260 | 0.8117 | +| 0.4038 | 0.2988 | 1160 | 0.4258 | 0.7736 | 0.8554 | 0.8554 | 0.4783 | 0.4783 | 0.8554 | 0.4783 | 0.6668 | 0.8167 | +| 0.3962 | 0.3001 | 1165 | 0.4369 | 0.7925 | 0.8506 | 0.8916 | 0.4348 | 0.5263 | 0.8706 | 0.4762 | 0.6632 | 0.8206 | +| 0.3823 | 0.3014 | 1170 | 0.4229 | 0.8019 | 0.8780 | 0.8675 | 0.5652 | 0.5417 | 0.8727 | 0.5532 | 0.7163 | 0.8146 | +| 0.3748 | 0.3027 | 1175 | 0.4378 | 0.8019 | 0.8605 | 0.8916 | 0.4783 | 0.55 | 0.8757 | 0.5116 | 0.6849 | 0.8012 | +| 0.3498 | 0.3040 | 1180 | 0.4444 | 0.8019 | 0.8605 | 0.8916 | 0.4783 | 0.55 | 0.8757 | 0.5116 | 0.6849 | 0.8028 | +| 0.3646 | 0.3053 | 1185 | 0.4636 | 0.7830 | 0.8488 | 0.8795 | 0.4348 | 0.5 | 0.8639 | 0.4651 | 0.6572 | 0.8096 | +| 0.4483 | 0.3065 | 1190 | 0.4649 | 0.7547 | 0.8434 | 0.8434 | 0.4348 | 0.4348 | 0.8434 | 0.4348 | 0.6391 | 0.8112 | +| 0.3717 | 0.3078 | 1195 | 0.4465 | 0.7547 | 0.8434 | 0.8434 | 0.4348 | 0.4348 | 0.8434 | 0.4348 | 0.6391 | 0.8146 | +| 0.3687 | 0.3091 | 1200 | 0.4538 | 0.7547 | 0.8353 | 0.8554 | 0.3913 | 0.4286 | 0.8452 | 0.4091 | 0.6234 | 0.8237 | +| 0.4613 | 0.3104 | 1205 | 0.4114 | 0.7642 | 0.8537 | 0.8434 | 0.4783 | 0.4583 | 0.8485 | 0.4681 | 0.6608 | 0.8334 | +| 0.3461 | 0.3117 | 1210 | 0.4274 | 0.7642 | 0.8372 | 0.8675 | 0.3913 | 0.45 | 0.8521 | 0.4186 | 0.6294 | 0.8311 | +| 0.2799 | 0.3130 | 1215 | 0.4193 | 0.7642 | 0.8372 | 0.8675 | 0.3913 | 0.45 | 0.8521 | 0.4186 | 0.6294 | 0.8363 | +| 0.3638 | 0.3143 | 1220 | 0.3781 | 0.7736 | 0.8642 | 0.8434 | 0.5217 | 0.48 | 0.8537 | 0.5 | 0.6826 | 0.8523 | +| 0.3624 | 0.3156 | 1225 | 0.4166 | 0.8019 | 0.8444 | 0.9157 | 0.3913 | 0.5625 | 0.8786 | 0.4615 | 0.6535 | 0.8449 | +| 0.3737 | 0.3168 | 1230 | 0.3821 | 0.7830 | 0.8571 | 0.8675 | 0.4783 | 0.5 | 0.8623 | 0.4889 | 0.6729 | 0.8413 | +| 0.342 | 0.3181 | 1235 | 0.3833 | 0.7925 | 0.8861 | 0.8434 | 0.6087 | 0.5185 | 0.8642 | 0.56 | 0.7260 | 0.8329 | +| 0.3311 | 0.3194 | 1240 | 0.4109 | 0.7642 | 0.8372 | 0.8675 | 0.3913 | 0.45 | 0.8521 | 0.4186 | 0.6294 | 0.8290 | +| 0.5057 | 0.3207 | 1245 | 0.4257 | 0.7925 | 0.8427 | 0.9036 | 0.3913 | 0.5294 | 0.8721 | 0.45 | 0.6475 | 0.8318 | +| 0.3819 | 0.3220 | 1250 | 0.4076 | 0.7830 | 0.875 | 0.8434 | 0.5652 | 0.5 | 0.8589 | 0.5306 | 0.7043 | 0.8290 | +| 0.3417 | 0.3233 | 1255 | 0.4085 | 0.7547 | 0.8434 | 0.8434 | 0.4348 | 0.4348 | 0.8434 | 0.4348 | 0.6391 | 0.8339 | +| 0.3151 | 0.3246 | 1260 | 0.4007 | 0.7925 | 0.8506 | 0.8916 | 0.4348 | 0.5263 | 0.8706 | 0.4762 | 0.6632 | 0.8371 | +| 0.3314 | 0.3259 | 1265 | 0.3908 | 0.8113 | 0.8795 | 0.8795 | 0.5652 | 0.5652 | 0.8795 | 0.5652 | 0.7224 | 0.8371 | +| 0.4088 | 0.3271 | 1270 | 0.3882 | 0.8208 | 0.8902 | 0.8795 | 0.6087 | 0.5833 | 0.8848 | 0.5957 | 0.7441 | 0.8434 | +| 0.4616 | 0.3284 | 1275 | 0.3855 | 0.8113 | 0.8795 | 0.8795 | 0.5652 | 0.5652 | 0.8795 | 0.5652 | 0.7224 | 0.8504 | +| 0.3316 | 0.3297 | 1280 | 0.3807 | 0.8302 | 0.8916 | 0.8916 | 0.6087 | 0.6087 | 0.8916 | 0.6087 | 0.7501 | 0.8533 | +| 0.3599 | 0.3310 | 1285 | 0.4018 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8596 | +| 0.3667 | 0.3323 | 1290 | 0.3860 | 0.8396 | 0.8929 | 0.9036 | 0.6087 | 0.6364 | 0.8982 | 0.6222 | 0.7562 | 0.8554 | +| 0.3687 | 0.3336 | 1295 | 0.3906 | 0.8208 | 0.8902 | 0.8795 | 0.6087 | 0.5833 | 0.8848 | 0.5957 | 0.7441 | 0.8442 | +| 0.3206 | 0.3349 | 1300 | 0.3991 | 0.8019 | 0.8780 | 0.8675 | 0.5652 | 0.5417 | 0.8727 | 0.5532 | 0.7163 | 0.8381 | +| 0.4156 | 0.3362 | 1305 | 0.4012 | 0.7925 | 0.8765 | 0.8554 | 0.5652 | 0.52 | 0.8659 | 0.5417 | 0.7103 | 0.8358 | +| 0.2879 | 0.3374 | 1310 | 0.4057 | 0.7925 | 0.8588 | 0.8795 | 0.4783 | 0.5238 | 0.8690 | 0.5 | 0.6789 | 0.8473 | +| 0.3817 | 0.3387 | 1315 | 0.4484 | 0.8208 | 0.8556 | 0.9277 | 0.4348 | 0.625 | 0.8902 | 0.5128 | 0.6812 | 0.8604 | +| 0.3692 | 0.3400 | 1320 | 0.4667 | 0.8396 | 0.8587 | 0.9518 | 0.4348 | 0.7143 | 0.9029 | 0.5405 | 0.6933 | 0.8628 | +| 0.2943 | 0.3413 | 1325 | 0.4061 | 0.8019 | 0.8605 | 0.8916 | 0.4783 | 0.55 | 0.8757 | 0.5116 | 0.6849 | 0.8669 | +| 0.3826 | 0.3426 | 1330 | 0.4068 | 0.8113 | 0.8621 | 0.9036 | 0.4783 | 0.5789 | 0.8824 | 0.5238 | 0.6909 | 0.8751 | +| 0.3109 | 0.3439 | 1335 | 0.3965 | 0.8302 | 0.8571 | 0.9398 | 0.4348 | 0.6667 | 0.8966 | 0.5263 | 0.6873 | 0.8761 | +| 0.4137 | 0.3452 | 1340 | 0.3946 | 0.8302 | 0.8571 | 0.9398 | 0.4348 | 0.6667 | 0.8966 | 0.5263 | 0.6873 | 0.8769 | +| 0.307 | 0.3465 | 1345 | 0.3609 | 0.8302 | 0.8916 | 0.8916 | 0.6087 | 0.6087 | 0.8916 | 0.6087 | 0.7501 | 0.8779 | +| 0.3801 | 0.3478 | 1350 | 0.3768 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8806 | +| 0.3575 | 0.3490 | 1355 | 0.4105 | 0.8302 | 0.8571 | 0.9398 | 0.4348 | 0.6667 | 0.8966 | 0.5263 | 0.6873 | 0.8829 | +| 0.4157 | 0.3503 | 1360 | 0.3599 | 0.8491 | 0.8941 | 0.9157 | 0.6087 | 0.6667 | 0.9048 | 0.6364 | 0.7622 | 0.8745 | +| 0.3056 | 0.3516 | 1365 | 0.3860 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8709 | +| 0.3009 | 0.3529 | 1370 | 0.4044 | 0.8396 | 0.8667 | 0.9398 | 0.4783 | 0.6875 | 0.9017 | 0.5641 | 0.7090 | 0.8748 | +| 0.3706 | 0.3542 | 1375 | 0.3684 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8714 | +| 0.3368 | 0.3555 | 1380 | 0.3609 | 0.8491 | 0.8941 | 0.9157 | 0.6087 | 0.6667 | 0.9048 | 0.6364 | 0.7622 | 0.8730 | +| 0.3739 | 0.3568 | 1385 | 0.3547 | 0.8585 | 0.8953 | 0.9277 | 0.6087 | 0.7 | 0.9112 | 0.6512 | 0.7682 | 0.8782 | +| 0.3435 | 0.3581 | 1390 | 0.3632 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8800 | +| 0.3578 | 0.3593 | 1395 | 0.3481 | 0.8491 | 0.9036 | 0.9036 | 0.6522 | 0.6522 | 0.9036 | 0.6522 | 0.7779 | 0.8790 | +| 0.3426 | 0.3606 | 1400 | 0.3831 | 0.8679 | 0.8876 | 0.9518 | 0.5652 | 0.7647 | 0.9186 | 0.65 | 0.7585 | 0.8803 | +| 0.4092 | 0.3619 | 1405 | 0.3696 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8769 | +| 0.4104 | 0.3632 | 1410 | 0.3517 | 0.8113 | 0.9091 | 0.8434 | 0.6957 | 0.5517 | 0.875 | 0.6154 | 0.7695 | 0.8732 | +| 0.4174 | 0.3645 | 1415 | 0.3603 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8761 | +| 0.4978 | 0.3658 | 1420 | 0.4500 | 0.8302 | 0.8351 | 0.9759 | 0.3043 | 0.7778 | 0.9 | 0.4375 | 0.6401 | 0.8811 | +| 0.4009 | 0.3671 | 1425 | 0.3744 | 0.8302 | 0.8652 | 0.9277 | 0.4783 | 0.6471 | 0.8953 | 0.55 | 0.7030 | 0.8782 | +| 0.4457 | 0.3684 | 1430 | 0.3689 | 0.8113 | 0.9091 | 0.8434 | 0.6957 | 0.5517 | 0.875 | 0.6154 | 0.7695 | 0.8722 | +| 0.3959 | 0.3696 | 1435 | 0.3935 | 0.8208 | 0.8556 | 0.9277 | 0.4348 | 0.625 | 0.8902 | 0.5128 | 0.6812 | 0.8756 | +| 0.2882 | 0.3709 | 1440 | 0.3977 | 0.8208 | 0.8556 | 0.9277 | 0.4348 | 0.625 | 0.8902 | 0.5128 | 0.6812 | 0.8777 | +| 0.3228 | 0.3722 | 1445 | 0.3726 | 0.8208 | 0.8810 | 0.8916 | 0.5652 | 0.5909 | 0.8862 | 0.5778 | 0.7284 | 0.8769 | +| 0.3377 | 0.3735 | 1450 | 0.3736 | 0.8302 | 0.8736 | 0.9157 | 0.5217 | 0.6316 | 0.8941 | 0.5714 | 0.7187 | 0.8811 | +| 0.3571 | 0.3748 | 1455 | 0.3561 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8874 | +| 0.3172 | 0.3761 | 1460 | 0.3526 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8858 | +| 0.3526 | 0.3774 | 1465 | 0.3563 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8800 | +| 0.3781 | 0.3787 | 1470 | 0.3561 | 0.8208 | 0.8902 | 0.8795 | 0.6087 | 0.5833 | 0.8848 | 0.5957 | 0.7441 | 0.8756 | +| 0.3863 | 0.3800 | 1475 | 0.3616 | 0.8208 | 0.8902 | 0.8795 | 0.6087 | 0.5833 | 0.8848 | 0.5957 | 0.7441 | 0.8722 | +| 0.3622 | 0.3812 | 1480 | 0.3797 | 0.8208 | 0.8636 | 0.9157 | 0.4783 | 0.6111 | 0.8889 | 0.5366 | 0.6970 | 0.8774 | +| 0.3054 | 0.3825 | 1485 | 0.3819 | 0.8208 | 0.8556 | 0.9277 | 0.4348 | 0.625 | 0.8902 | 0.5128 | 0.6812 | 0.8701 | +| 0.4206 | 0.3838 | 1490 | 0.3865 | 0.8302 | 0.8652 | 0.9277 | 0.4783 | 0.6471 | 0.8953 | 0.55 | 0.7030 | 0.8667 | +| 0.3527 | 0.3851 | 1495 | 0.3869 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8594 | +| 0.319 | 0.3864 | 1500 | 0.3952 | 0.8208 | 0.8556 | 0.9277 | 0.4348 | 0.625 | 0.8902 | 0.5128 | 0.6812 | 0.8667 | +| 0.2939 | 0.3877 | 1505 | 0.3835 | 0.8208 | 0.8902 | 0.8795 | 0.6087 | 0.5833 | 0.8848 | 0.5957 | 0.7441 | 0.8622 | +| 0.3062 | 0.3890 | 1510 | 0.3812 | 0.8113 | 0.8795 | 0.8795 | 0.5652 | 0.5652 | 0.8795 | 0.5652 | 0.7224 | 0.8683 | +| 0.3631 | 0.3903 | 1515 | 0.3757 | 0.8302 | 0.8736 | 0.9157 | 0.5217 | 0.6316 | 0.8941 | 0.5714 | 0.7187 | 0.8756 | +| 0.4595 | 0.3915 | 1520 | 0.3516 | 0.8396 | 0.9024 | 0.8916 | 0.6522 | 0.625 | 0.8970 | 0.6383 | 0.7719 | 0.8824 | +| 0.3576 | 0.3928 | 1525 | 0.3596 | 0.8302 | 0.8916 | 0.8916 | 0.6087 | 0.6087 | 0.8916 | 0.6087 | 0.7501 | 0.8803 | +| 0.3184 | 0.3941 | 1530 | 0.4115 | 0.8585 | 0.8696 | 0.9639 | 0.4783 | 0.7857 | 0.9143 | 0.5946 | 0.7211 | 0.8732 | +| 0.3171 | 0.3954 | 1535 | 0.4240 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8552 | +| 0.4037 | 0.3967 | 1540 | 0.3970 | 0.8396 | 0.8929 | 0.9036 | 0.6087 | 0.6364 | 0.8982 | 0.6222 | 0.7562 | 0.8549 | +| 0.3857 | 0.3980 | 1545 | 0.3821 | 0.8208 | 0.8902 | 0.8795 | 0.6087 | 0.5833 | 0.8848 | 0.5957 | 0.7441 | 0.8554 | +| 0.381 | 0.3993 | 1550 | 0.3999 | 0.8491 | 0.8941 | 0.9157 | 0.6087 | 0.6667 | 0.9048 | 0.6364 | 0.7622 | 0.8633 | +| 0.3599 | 0.4006 | 1555 | 0.3954 | 0.8491 | 0.8941 | 0.9157 | 0.6087 | 0.6667 | 0.9048 | 0.6364 | 0.7622 | 0.8638 | +| 0.4155 | 0.4018 | 1560 | 0.3821 | 0.8113 | 0.8889 | 0.8675 | 0.6087 | 0.56 | 0.8780 | 0.5833 | 0.7381 | 0.8609 | +| 0.3233 | 0.4031 | 1565 | 0.3837 | 0.8302 | 0.8736 | 0.9157 | 0.5217 | 0.6316 | 0.8941 | 0.5714 | 0.7187 | 0.8688 | +| 0.4813 | 0.4044 | 1570 | 0.4035 | 0.8302 | 0.8495 | 0.9518 | 0.3913 | 0.6923 | 0.8977 | 0.5 | 0.6716 | 0.8719 | +| 0.2816 | 0.4057 | 1575 | 0.3760 | 0.8302 | 0.8736 | 0.9157 | 0.5217 | 0.6316 | 0.8941 | 0.5714 | 0.7187 | 0.8743 | +| 0.4326 | 0.4070 | 1580 | 0.3638 | 0.8491 | 0.8941 | 0.9157 | 0.6087 | 0.6667 | 0.9048 | 0.6364 | 0.7622 | 0.8790 | +| 0.3014 | 0.4083 | 1585 | 0.3692 | 0.8585 | 0.8953 | 0.9277 | 0.6087 | 0.7 | 0.9112 | 0.6512 | 0.7682 | 0.8863 | +| 0.2682 | 0.4096 | 1590 | 0.3783 | 0.8585 | 0.8778 | 0.9518 | 0.5217 | 0.75 | 0.9133 | 0.6154 | 0.7368 | 0.8874 | +| 0.3469 | 0.4109 | 1595 | 0.3479 | 0.8585 | 0.8953 | 0.9277 | 0.6087 | 0.7 | 0.9112 | 0.6512 | 0.7682 | 0.8908 | +| 0.4717 | 0.4122 | 1600 | 0.3249 | 0.8302 | 0.9012 | 0.8795 | 0.6522 | 0.6 | 0.8902 | 0.625 | 0.7658 | 0.8937 | +| 0.3313 | 0.4134 | 1605 | 0.3273 | 0.8396 | 0.9024 | 0.8916 | 0.6522 | 0.625 | 0.8970 | 0.6383 | 0.7719 | 0.8994 | +| 0.2726 | 0.4147 | 1610 | 0.3318 | 0.8491 | 0.9036 | 0.9036 | 0.6522 | 0.6522 | 0.9036 | 0.6522 | 0.7779 | 0.8979 | +| 0.4087 | 0.4160 | 1615 | 0.3299 | 0.8491 | 0.9036 | 0.9036 | 0.6522 | 0.6522 | 0.9036 | 0.6522 | 0.7779 | 0.8916 | +| 0.326 | 0.4173 | 1620 | 0.3261 | 0.8491 | 0.9036 | 0.9036 | 0.6522 | 0.6522 | 0.9036 | 0.6522 | 0.7779 | 0.8955 | +| 0.3205 | 0.4186 | 1625 | 0.3175 | 0.8585 | 0.9146 | 0.9036 | 0.6957 | 0.6667 | 0.9091 | 0.6809 | 0.7996 | 0.8916 | +| 0.4096 | 0.4199 | 1630 | 0.3265 | 0.8491 | 0.9136 | 0.8916 | 0.6957 | 0.64 | 0.9024 | 0.6667 | 0.7936 | 0.8905 | +| 0.4521 | 0.4212 | 1635 | 0.3312 | 0.8585 | 0.9048 | 0.9157 | 0.6522 | 0.6818 | 0.9102 | 0.6667 | 0.7839 | 0.8926 | +| 0.3802 | 0.4225 | 1640 | 0.3475 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8910 | +| 0.2765 | 0.4237 | 1645 | 0.3702 | 0.8396 | 0.8667 | 0.9398 | 0.4783 | 0.6875 | 0.9017 | 0.5641 | 0.7090 | 0.8879 | +| 0.2994 | 0.4250 | 1650 | 0.3558 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8837 | +| 0.3535 | 0.4263 | 1655 | 0.3607 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8766 | +| 0.4246 | 0.4276 | 1660 | 0.3644 | 0.8396 | 0.8929 | 0.9036 | 0.6087 | 0.6364 | 0.8982 | 0.6222 | 0.7562 | 0.8654 | +| 0.3036 | 0.4289 | 1665 | 0.3633 | 0.8113 | 0.8889 | 0.8675 | 0.6087 | 0.56 | 0.8780 | 0.5833 | 0.7381 | 0.8612 | +| 0.2816 | 0.4302 | 1670 | 0.3867 | 0.8113 | 0.8539 | 0.9157 | 0.4348 | 0.5882 | 0.8837 | 0.5 | 0.6752 | 0.8607 | +| 0.3937 | 0.4315 | 1675 | 0.3591 | 0.8019 | 0.8875 | 0.8554 | 0.6087 | 0.5385 | 0.8712 | 0.5714 | 0.7321 | 0.8662 | +| 0.3493 | 0.4328 | 1680 | 0.3547 | 0.8113 | 0.8987 | 0.8554 | 0.6522 | 0.5556 | 0.8765 | 0.6 | 0.7538 | 0.8711 | +| 0.3517 | 0.4340 | 1685 | 0.3674 | 0.8585 | 0.8778 | 0.9518 | 0.5217 | 0.75 | 0.9133 | 0.6154 | 0.7368 | 0.8848 | +| 0.3213 | 0.4353 | 1690 | 0.3577 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8882 | +| 0.4174 | 0.4366 | 1695 | 0.3705 | 0.7642 | 0.9265 | 0.7590 | 0.7826 | 0.4737 | 0.8344 | 0.5902 | 0.7708 | 0.8717 | +| 0.429 | 0.4379 | 1700 | 0.3701 | 0.7925 | 0.9296 | 0.7952 | 0.7826 | 0.5143 | 0.8571 | 0.6207 | 0.7889 | 0.8714 | +| 0.3844 | 0.4392 | 1705 | 0.3737 | 0.8208 | 0.8556 | 0.9277 | 0.4348 | 0.625 | 0.8902 | 0.5128 | 0.6812 | 0.8769 | +| 0.3729 | 0.4405 | 1710 | 0.3863 | 0.8208 | 0.8556 | 0.9277 | 0.4348 | 0.625 | 0.8902 | 0.5128 | 0.6812 | 0.8756 | +| 0.3991 | 0.4418 | 1715 | 0.3629 | 0.8113 | 0.92 | 0.8313 | 0.7391 | 0.5484 | 0.8734 | 0.6296 | 0.7852 | 0.8675 | +| 0.3854 | 0.4431 | 1720 | 0.3659 | 0.7925 | 0.8961 | 0.8313 | 0.6522 | 0.5172 | 0.8625 | 0.5769 | 0.7417 | 0.8672 | +| 0.2113 | 0.4444 | 1725 | 0.3866 | 0.8019 | 0.8523 | 0.9036 | 0.4348 | 0.5556 | 0.8772 | 0.4878 | 0.6692 | 0.8630 | +| 0.3539 | 0.4456 | 1730 | 0.3855 | 0.8208 | 0.8636 | 0.9157 | 0.4783 | 0.6111 | 0.8889 | 0.5366 | 0.6970 | 0.8641 | +| 0.2872 | 0.4469 | 1735 | 0.3746 | 0.8019 | 0.8605 | 0.8916 | 0.4783 | 0.55 | 0.8757 | 0.5116 | 0.6849 | 0.8654 | +| 0.3908 | 0.4482 | 1740 | 0.3653 | 0.7925 | 0.8861 | 0.8434 | 0.6087 | 0.5185 | 0.8642 | 0.56 | 0.7260 | 0.8732 | +| 0.3748 | 0.4495 | 1745 | 0.3757 | 0.8113 | 0.8539 | 0.9157 | 0.4348 | 0.5882 | 0.8837 | 0.5 | 0.6752 | 0.8803 | +| 0.2988 | 0.4508 | 1750 | 0.3680 | 0.8208 | 0.8902 | 0.8795 | 0.6087 | 0.5833 | 0.8848 | 0.5957 | 0.7441 | 0.8756 | +| 0.31 | 0.4521 | 1755 | 0.3652 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8787 | +| 0.36 | 0.4534 | 1760 | 0.3655 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8819 | +| 0.3475 | 0.4547 | 1765 | 0.3603 | 0.8302 | 0.8916 | 0.8916 | 0.6087 | 0.6087 | 0.8916 | 0.6087 | 0.7501 | 0.8871 | +| 0.4313 | 0.4559 | 1770 | 0.3612 | 0.8113 | 0.8706 | 0.8916 | 0.5217 | 0.5714 | 0.8810 | 0.5455 | 0.7067 | 0.8874 | +| 0.4534 | 0.4572 | 1775 | 0.3524 | 0.8208 | 0.8902 | 0.8795 | 0.6087 | 0.5833 | 0.8848 | 0.5957 | 0.7441 | 0.8887 | +| 0.2876 | 0.4585 | 1780 | 0.3470 | 0.8113 | 0.8795 | 0.8795 | 0.5652 | 0.5652 | 0.8795 | 0.5652 | 0.7224 | 0.8924 | +| 0.3135 | 0.4598 | 1785 | 0.3470 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8952 | +| 0.3132 | 0.4611 | 1790 | 0.3421 | 0.8019 | 0.8875 | 0.8554 | 0.6087 | 0.5385 | 0.8712 | 0.5714 | 0.7321 | 0.8900 | +| 0.3193 | 0.4624 | 1795 | 0.3416 | 0.8208 | 0.8902 | 0.8795 | 0.6087 | 0.5833 | 0.8848 | 0.5957 | 0.7441 | 0.8913 | +| 0.3846 | 0.4637 | 1800 | 0.3524 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8905 | +| 0.408 | 0.4650 | 1805 | 0.3586 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8845 | +| 0.3095 | 0.4662 | 1810 | 0.3692 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8790 | +| 0.3876 | 0.4675 | 1815 | 0.3795 | 0.8208 | 0.8721 | 0.9036 | 0.5217 | 0.6 | 0.8876 | 0.5581 | 0.7127 | 0.8738 | +| 0.3471 | 0.4688 | 1820 | 0.3854 | 0.8113 | 0.8706 | 0.8916 | 0.5217 | 0.5714 | 0.8810 | 0.5455 | 0.7067 | 0.8635 | +| 0.3671 | 0.4701 | 1825 | 0.3926 | 0.8208 | 0.8721 | 0.9036 | 0.5217 | 0.6 | 0.8876 | 0.5581 | 0.7127 | 0.8594 | +| 0.3548 | 0.4714 | 1830 | 0.3927 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8544 | +| 0.3817 | 0.4727 | 1835 | 0.3874 | 0.7830 | 0.875 | 0.8434 | 0.5652 | 0.5 | 0.8589 | 0.5306 | 0.7043 | 0.8533 | +| 0.4201 | 0.4740 | 1840 | 0.4082 | 0.8113 | 0.8621 | 0.9036 | 0.4783 | 0.5789 | 0.8824 | 0.5238 | 0.6909 | 0.8604 | +| 0.3704 | 0.4753 | 1845 | 0.4135 | 0.8302 | 0.8652 | 0.9277 | 0.4783 | 0.6471 | 0.8953 | 0.55 | 0.7030 | 0.8620 | +| 0.3185 | 0.4766 | 1850 | 0.3891 | 0.8208 | 0.8810 | 0.8916 | 0.5652 | 0.5909 | 0.8862 | 0.5778 | 0.7284 | 0.8641 | +| 0.3323 | 0.4778 | 1855 | 0.3746 | 0.8019 | 0.8875 | 0.8554 | 0.6087 | 0.5385 | 0.8712 | 0.5714 | 0.7321 | 0.8656 | +| 0.3611 | 0.4791 | 1860 | 0.3755 | 0.8113 | 0.8889 | 0.8675 | 0.6087 | 0.56 | 0.8780 | 0.5833 | 0.7381 | 0.8646 | +| 0.4048 | 0.4804 | 1865 | 0.3894 | 0.8208 | 0.8810 | 0.8916 | 0.5652 | 0.5909 | 0.8862 | 0.5778 | 0.7284 | 0.8641 | +| 0.3448 | 0.4817 | 1870 | 0.3881 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8656 | +| 0.3847 | 0.4830 | 1875 | 0.3809 | 0.8302 | 0.8916 | 0.8916 | 0.6087 | 0.6087 | 0.8916 | 0.6087 | 0.7501 | 0.8672 | +| 0.2915 | 0.4843 | 1880 | 0.4040 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8722 | +| 0.3303 | 0.4856 | 1885 | 0.4034 | 0.8208 | 0.8636 | 0.9157 | 0.4783 | 0.6111 | 0.8889 | 0.5366 | 0.6970 | 0.8751 | +| 0.3504 | 0.4869 | 1890 | 0.4103 | 0.8208 | 0.8636 | 0.9157 | 0.4783 | 0.6111 | 0.8889 | 0.5366 | 0.6970 | 0.8709 | +| 0.3831 | 0.4881 | 1895 | 0.3815 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8730 | +| 0.3339 | 0.4894 | 1900 | 0.3779 | 0.8302 | 0.8916 | 0.8916 | 0.6087 | 0.6087 | 0.8916 | 0.6087 | 0.7501 | 0.8669 | +| 0.3743 | 0.4907 | 1905 | 0.3910 | 0.8208 | 0.8721 | 0.9036 | 0.5217 | 0.6 | 0.8876 | 0.5581 | 0.7127 | 0.8685 | +| 0.3391 | 0.4920 | 1910 | 0.3922 | 0.8208 | 0.8721 | 0.9036 | 0.5217 | 0.6 | 0.8876 | 0.5581 | 0.7127 | 0.8753 | +| 0.4081 | 0.4933 | 1915 | 0.3621 | 0.8208 | 0.8810 | 0.8916 | 0.5652 | 0.5909 | 0.8862 | 0.5778 | 0.7284 | 0.8764 | +| 0.3946 | 0.4946 | 1920 | 0.3613 | 0.8302 | 0.8916 | 0.8916 | 0.6087 | 0.6087 | 0.8916 | 0.6087 | 0.7501 | 0.8787 | +| 0.3585 | 0.4959 | 1925 | 0.3826 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8819 | +| 0.3471 | 0.4972 | 1930 | 0.3887 | 0.8302 | 0.8652 | 0.9277 | 0.4783 | 0.6471 | 0.8953 | 0.55 | 0.7030 | 0.8798 | +| 0.2986 | 0.4984 | 1935 | 0.3855 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8793 | +| 0.4374 | 0.4997 | 1940 | 0.3760 | 0.8113 | 0.8889 | 0.8675 | 0.6087 | 0.56 | 0.8780 | 0.5833 | 0.7381 | 0.8714 | +| 0.2904 | 0.5010 | 1945 | 0.3890 | 0.8208 | 0.8902 | 0.8795 | 0.6087 | 0.5833 | 0.8848 | 0.5957 | 0.7441 | 0.8667 | +| 0.4665 | 0.5023 | 1950 | 0.4582 | 0.7925 | 0.8211 | 0.9398 | 0.2609 | 0.5455 | 0.8764 | 0.3529 | 0.6003 | 0.8669 | +| 0.2841 | 0.5036 | 1955 | 0.4416 | 0.8302 | 0.8571 | 0.9398 | 0.4348 | 0.6667 | 0.8966 | 0.5263 | 0.6873 | 0.8693 | +| 0.3671 | 0.5049 | 1960 | 0.3840 | 0.8019 | 0.8780 | 0.8675 | 0.5652 | 0.5417 | 0.8727 | 0.5532 | 0.7163 | 0.8633 | +| 0.3839 | 0.5062 | 1965 | 0.3800 | 0.7925 | 0.8861 | 0.8434 | 0.6087 | 0.5185 | 0.8642 | 0.56 | 0.7260 | 0.8641 | +| 0.3481 | 0.5075 | 1970 | 0.3817 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8690 | +| 0.4024 | 0.5088 | 1975 | 0.4212 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8727 | +| 0.3581 | 0.5100 | 1980 | 0.3865 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8680 | +| 0.3977 | 0.5113 | 1985 | 0.3699 | 0.8208 | 0.8902 | 0.8795 | 0.6087 | 0.5833 | 0.8848 | 0.5957 | 0.7441 | 0.8659 | +| 0.4053 | 0.5126 | 1990 | 0.3803 | 0.8302 | 0.8916 | 0.8916 | 0.6087 | 0.6087 | 0.8916 | 0.6087 | 0.7501 | 0.8651 | +| 0.3561 | 0.5139 | 1995 | 0.4030 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8659 | +| 0.3085 | 0.5152 | 2000 | 0.3987 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8654 | +| 0.4248 | 0.5165 | 2005 | 0.3814 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8675 | +| 0.3487 | 0.5178 | 2010 | 0.3726 | 0.8302 | 0.8916 | 0.8916 | 0.6087 | 0.6087 | 0.8916 | 0.6087 | 0.7501 | 0.8690 | +| 0.3711 | 0.5191 | 2015 | 0.3747 | 0.8208 | 0.8810 | 0.8916 | 0.5652 | 0.5909 | 0.8862 | 0.5778 | 0.7284 | 0.8669 | +| 0.4005 | 0.5203 | 2020 | 0.3947 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8643 | +| 0.3797 | 0.5216 | 2025 | 0.3900 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8622 | +| 0.2988 | 0.5229 | 2030 | 0.3836 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8617 | +| 0.4445 | 0.5242 | 2035 | 0.3791 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8667 | +| 0.3014 | 0.5255 | 2040 | 0.3816 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8667 | +| 0.3094 | 0.5268 | 2045 | 0.4108 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8638 | +| 0.3236 | 0.5281 | 2050 | 0.3884 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8633 | +| 0.4093 | 0.5294 | 2055 | 0.3760 | 0.8019 | 0.8780 | 0.8675 | 0.5652 | 0.5417 | 0.8727 | 0.5532 | 0.7163 | 0.8588 | +| 0.3514 | 0.5306 | 2060 | 0.3929 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8609 | +| 0.347 | 0.5319 | 2065 | 0.3846 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8559 | +| 0.4321 | 0.5332 | 2070 | 0.3770 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8601 | +| 0.2823 | 0.5345 | 2075 | 0.3931 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8559 | +| 0.4027 | 0.5358 | 2080 | 0.3843 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8588 | +| 0.2637 | 0.5371 | 2085 | 0.4116 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8630 | +| 0.4636 | 0.5384 | 2090 | 0.4114 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8649 | +| 0.3848 | 0.5397 | 2095 | 0.3693 | 0.8208 | 0.8810 | 0.8916 | 0.5652 | 0.5909 | 0.8862 | 0.5778 | 0.7284 | 0.8612 | +| 0.2583 | 0.5409 | 2100 | 0.3630 | 0.8208 | 0.8902 | 0.8795 | 0.6087 | 0.5833 | 0.8848 | 0.5957 | 0.7441 | 0.8625 | +| 0.3542 | 0.5422 | 2105 | 0.3848 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8601 | +| 0.4379 | 0.5435 | 2110 | 0.4011 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8622 | +| 0.3062 | 0.5448 | 2115 | 0.3659 | 0.8208 | 0.8810 | 0.8916 | 0.5652 | 0.5909 | 0.8862 | 0.5778 | 0.7284 | 0.8583 | +| 0.3181 | 0.5461 | 2120 | 0.3646 | 0.8208 | 0.8902 | 0.8795 | 0.6087 | 0.5833 | 0.8848 | 0.5957 | 0.7441 | 0.8578 | +| 0.3141 | 0.5474 | 2125 | 0.3723 | 0.8208 | 0.8810 | 0.8916 | 0.5652 | 0.5909 | 0.8862 | 0.5778 | 0.7284 | 0.8596 | +| 0.352 | 0.5487 | 2130 | 0.3738 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8630 | +| 0.3565 | 0.5500 | 2135 | 0.3782 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8669 | +| 0.3461 | 0.5513 | 2140 | 0.3741 | 0.8208 | 0.8810 | 0.8916 | 0.5652 | 0.5909 | 0.8862 | 0.5778 | 0.7284 | 0.8609 | +| 0.3681 | 0.5525 | 2145 | 0.3741 | 0.8113 | 0.8706 | 0.8916 | 0.5217 | 0.5714 | 0.8810 | 0.5455 | 0.7067 | 0.8641 | +| 0.3291 | 0.5538 | 2150 | 0.3932 | 0.8208 | 0.8636 | 0.9157 | 0.4783 | 0.6111 | 0.8889 | 0.5366 | 0.6970 | 0.8635 | +| 0.3096 | 0.5551 | 2155 | 0.3714 | 0.8208 | 0.8721 | 0.9036 | 0.5217 | 0.6 | 0.8876 | 0.5581 | 0.7127 | 0.8617 | +| 0.4049 | 0.5564 | 2160 | 0.3766 | 0.8208 | 0.8721 | 0.9036 | 0.5217 | 0.6 | 0.8876 | 0.5581 | 0.7127 | 0.8630 | +| 0.378 | 0.5577 | 2165 | 0.3979 | 0.8302 | 0.8652 | 0.9277 | 0.4783 | 0.6471 | 0.8953 | 0.55 | 0.7030 | 0.8683 | +| 0.3686 | 0.5590 | 2170 | 0.3800 | 0.8302 | 0.8736 | 0.9157 | 0.5217 | 0.6316 | 0.8941 | 0.5714 | 0.7187 | 0.8662 | +| 0.3201 | 0.5603 | 2175 | 0.3686 | 0.8113 | 0.8706 | 0.8916 | 0.5217 | 0.5714 | 0.8810 | 0.5455 | 0.7067 | 0.8633 | +| 0.4344 | 0.5616 | 2180 | 0.3783 | 0.8302 | 0.8736 | 0.9157 | 0.5217 | 0.6316 | 0.8941 | 0.5714 | 0.7187 | 0.8656 | +| 0.3911 | 0.5628 | 2185 | 0.4005 | 0.8208 | 0.8636 | 0.9157 | 0.4783 | 0.6111 | 0.8889 | 0.5366 | 0.6970 | 0.8617 | +| 0.3033 | 0.5641 | 2190 | 0.3857 | 0.8208 | 0.8636 | 0.9157 | 0.4783 | 0.6111 | 0.8889 | 0.5366 | 0.6970 | 0.8604 | +| 0.3469 | 0.5654 | 2195 | 0.3751 | 0.8113 | 0.8706 | 0.8916 | 0.5217 | 0.5714 | 0.8810 | 0.5455 | 0.7067 | 0.8594 | +| 0.3133 | 0.5667 | 2200 | 0.3731 | 0.8302 | 0.8916 | 0.8916 | 0.6087 | 0.6087 | 0.8916 | 0.6087 | 0.7501 | 0.8588 | +| 0.325 | 0.5680 | 2205 | 0.3809 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8635 | +| 0.4481 | 0.5693 | 2210 | 0.3966 | 0.8302 | 0.8736 | 0.9157 | 0.5217 | 0.6316 | 0.8941 | 0.5714 | 0.7187 | 0.8604 | +| 0.3264 | 0.5706 | 2215 | 0.3808 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8588 | +| 0.3644 | 0.5719 | 2220 | 0.3738 | 0.8302 | 0.8916 | 0.8916 | 0.6087 | 0.6087 | 0.8916 | 0.6087 | 0.7501 | 0.8633 | +| 0.3197 | 0.5731 | 2225 | 0.3796 | 0.8208 | 0.8810 | 0.8916 | 0.5652 | 0.5909 | 0.8862 | 0.5778 | 0.7284 | 0.8594 | +| 0.3829 | 0.5744 | 2230 | 0.3940 | 0.8302 | 0.8736 | 0.9157 | 0.5217 | 0.6316 | 0.8941 | 0.5714 | 0.7187 | 0.8565 | +| 0.3284 | 0.5757 | 2235 | 0.3902 | 0.8208 | 0.8721 | 0.9036 | 0.5217 | 0.6 | 0.8876 | 0.5581 | 0.7127 | 0.8575 | +| 0.3922 | 0.5770 | 2240 | 0.3946 | 0.8208 | 0.8721 | 0.9036 | 0.5217 | 0.6 | 0.8876 | 0.5581 | 0.7127 | 0.8523 | +| 0.3295 | 0.5783 | 2245 | 0.4006 | 0.8302 | 0.8736 | 0.9157 | 0.5217 | 0.6316 | 0.8941 | 0.5714 | 0.7187 | 0.8512 | +| 0.3484 | 0.5796 | 2250 | 0.3955 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8528 | +| 0.4155 | 0.5809 | 2255 | 0.3812 | 0.8302 | 0.8736 | 0.9157 | 0.5217 | 0.6316 | 0.8941 | 0.5714 | 0.7187 | 0.8570 | +| 0.3749 | 0.5822 | 2260 | 0.3781 | 0.8208 | 0.8810 | 0.8916 | 0.5652 | 0.5909 | 0.8862 | 0.5778 | 0.7284 | 0.8573 | +| 0.368 | 0.5835 | 2265 | 0.4030 | 0.8396 | 0.8667 | 0.9398 | 0.4783 | 0.6875 | 0.9017 | 0.5641 | 0.7090 | 0.8549 | +| 0.386 | 0.5847 | 2270 | 0.4194 | 0.8396 | 0.8667 | 0.9398 | 0.4783 | 0.6875 | 0.9017 | 0.5641 | 0.7090 | 0.8510 | +| 0.3571 | 0.5860 | 2275 | 0.3899 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8502 | +| 0.3465 | 0.5873 | 2280 | 0.3781 | 0.8302 | 0.8736 | 0.9157 | 0.5217 | 0.6316 | 0.8941 | 0.5714 | 0.7187 | 0.8554 | +| 0.3596 | 0.5886 | 2285 | 0.3818 | 0.8208 | 0.8721 | 0.9036 | 0.5217 | 0.6 | 0.8876 | 0.5581 | 0.7127 | 0.8559 | +| 0.3866 | 0.5899 | 2290 | 0.3909 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8601 | +| 0.4089 | 0.5912 | 2295 | 0.3815 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8638 | +| 0.3049 | 0.5925 | 2300 | 0.3549 | 0.8302 | 0.8916 | 0.8916 | 0.6087 | 0.6087 | 0.8916 | 0.6087 | 0.7501 | 0.8646 | +| 0.3299 | 0.5938 | 2305 | 0.3506 | 0.8302 | 0.8916 | 0.8916 | 0.6087 | 0.6087 | 0.8916 | 0.6087 | 0.7501 | 0.8654 | +| 0.3794 | 0.5950 | 2310 | 0.3498 | 0.8302 | 0.8916 | 0.8916 | 0.6087 | 0.6087 | 0.8916 | 0.6087 | 0.7501 | 0.8704 | +| 0.3436 | 0.5963 | 2315 | 0.3612 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8690 | +| 0.3243 | 0.5976 | 2320 | 0.3590 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8696 | +| 0.2859 | 0.5989 | 2325 | 0.3634 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8696 | +| 0.3778 | 0.6002 | 2330 | 0.3773 | 0.8585 | 0.8778 | 0.9518 | 0.5217 | 0.75 | 0.9133 | 0.6154 | 0.7368 | 0.8732 | +| 0.3405 | 0.6015 | 2335 | 0.3560 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8704 | +| 0.3815 | 0.6028 | 2340 | 0.3513 | 0.8208 | 0.8810 | 0.8916 | 0.5652 | 0.5909 | 0.8862 | 0.5778 | 0.7284 | 0.8659 | +| 0.3316 | 0.6041 | 2345 | 0.3627 | 0.8208 | 0.8721 | 0.9036 | 0.5217 | 0.6 | 0.8876 | 0.5581 | 0.7127 | 0.8622 | +| 0.3666 | 0.6053 | 2350 | 0.3796 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8638 | +| 0.3383 | 0.6066 | 2355 | 0.3904 | 0.8396 | 0.8667 | 0.9398 | 0.4783 | 0.6875 | 0.9017 | 0.5641 | 0.7090 | 0.8575 | +| 0.2977 | 0.6079 | 2360 | 0.3845 | 0.8208 | 0.8636 | 0.9157 | 0.4783 | 0.6111 | 0.8889 | 0.5366 | 0.6970 | 0.8528 | +| 0.338 | 0.6092 | 2365 | 0.3889 | 0.8208 | 0.8636 | 0.9157 | 0.4783 | 0.6111 | 0.8889 | 0.5366 | 0.6970 | 0.8481 | +| 0.2856 | 0.6105 | 2370 | 0.4031 | 0.8113 | 0.8539 | 0.9157 | 0.4348 | 0.5882 | 0.8837 | 0.5 | 0.6752 | 0.8431 | +| 0.377 | 0.6118 | 2375 | 0.4336 | 0.8302 | 0.8571 | 0.9398 | 0.4348 | 0.6667 | 0.8966 | 0.5263 | 0.6873 | 0.8394 | +| 0.3363 | 0.6131 | 2380 | 0.4224 | 0.8302 | 0.8571 | 0.9398 | 0.4348 | 0.6667 | 0.8966 | 0.5263 | 0.6873 | 0.8434 | +| 0.3891 | 0.6144 | 2385 | 0.3949 | 0.8113 | 0.8621 | 0.9036 | 0.4783 | 0.5789 | 0.8824 | 0.5238 | 0.6909 | 0.8392 | +| 0.3405 | 0.6157 | 2390 | 0.4038 | 0.8302 | 0.8652 | 0.9277 | 0.4783 | 0.6471 | 0.8953 | 0.55 | 0.7030 | 0.8402 | +| 0.3433 | 0.6169 | 2395 | 0.4208 | 0.8302 | 0.8571 | 0.9398 | 0.4348 | 0.6667 | 0.8966 | 0.5263 | 0.6873 | 0.8394 | +| 0.3465 | 0.6182 | 2400 | 0.4336 | 0.8396 | 0.8587 | 0.9518 | 0.4348 | 0.7143 | 0.9029 | 0.5405 | 0.6933 | 0.8400 | +| 0.3185 | 0.6195 | 2405 | 0.4201 | 0.8396 | 0.8587 | 0.9518 | 0.4348 | 0.7143 | 0.9029 | 0.5405 | 0.6933 | 0.8442 | +| 0.3578 | 0.6208 | 2410 | 0.4000 | 0.8491 | 0.8681 | 0.9518 | 0.4783 | 0.7333 | 0.9080 | 0.5789 | 0.7150 | 0.8470 | +| 0.3382 | 0.6221 | 2415 | 0.3933 | 0.8302 | 0.8652 | 0.9277 | 0.4783 | 0.6471 | 0.8953 | 0.55 | 0.7030 | 0.8531 | +| 0.3768 | 0.6234 | 2420 | 0.3798 | 0.8208 | 0.8636 | 0.9157 | 0.4783 | 0.6111 | 0.8889 | 0.5366 | 0.6970 | 0.8596 | +| 0.3073 | 0.6247 | 2425 | 0.3815 | 0.8208 | 0.8636 | 0.9157 | 0.4783 | 0.6111 | 0.8889 | 0.5366 | 0.6970 | 0.8567 | +| 0.3273 | 0.6260 | 2430 | 0.3787 | 0.8208 | 0.8636 | 0.9157 | 0.4783 | 0.6111 | 0.8889 | 0.5366 | 0.6970 | 0.8601 | +| 0.2413 | 0.6272 | 2435 | 0.3718 | 0.8396 | 0.8667 | 0.9398 | 0.4783 | 0.6875 | 0.9017 | 0.5641 | 0.7090 | 0.8651 | +| 0.2826 | 0.6285 | 2440 | 0.3844 | 0.8585 | 0.8696 | 0.9639 | 0.4783 | 0.7857 | 0.9143 | 0.5946 | 0.7211 | 0.8743 | +| 0.3427 | 0.6298 | 2445 | 0.3750 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8774 | +| 0.2628 | 0.6311 | 2450 | 0.3538 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8795 | +| 0.2892 | 0.6324 | 2455 | 0.3533 | 0.8585 | 0.8778 | 0.9518 | 0.5217 | 0.75 | 0.9133 | 0.6154 | 0.7368 | 0.8866 | +| 0.2897 | 0.6337 | 2460 | 0.3626 | 0.8491 | 0.8681 | 0.9518 | 0.4783 | 0.7333 | 0.9080 | 0.5789 | 0.7150 | 0.8884 | +| 0.3011 | 0.6350 | 2465 | 0.3668 | 0.8491 | 0.8681 | 0.9518 | 0.4783 | 0.7333 | 0.9080 | 0.5789 | 0.7150 | 0.8908 | +| 0.3743 | 0.6363 | 2470 | 0.3669 | 0.8491 | 0.8681 | 0.9518 | 0.4783 | 0.7333 | 0.9080 | 0.5789 | 0.7150 | 0.8861 | +| 0.3917 | 0.6375 | 2475 | 0.3539 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8863 | +| 0.2568 | 0.6388 | 2480 | 0.3596 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8798 | +| 0.4075 | 0.6401 | 2485 | 0.3734 | 0.8302 | 0.8652 | 0.9277 | 0.4783 | 0.6471 | 0.8953 | 0.55 | 0.7030 | 0.8779 | +| 0.3179 | 0.6414 | 2490 | 0.3642 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8719 | +| 0.3516 | 0.6427 | 2495 | 0.3542 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8738 | +| 0.2655 | 0.6440 | 2500 | 0.3416 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8793 | +| 0.2999 | 0.6453 | 2505 | 0.3442 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8808 | +| 0.397 | 0.6466 | 2510 | 0.3348 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8866 | +| 0.2856 | 0.6479 | 2515 | 0.3209 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8861 | +| 0.4096 | 0.6491 | 2520 | 0.3218 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8887 | +| 0.3651 | 0.6504 | 2525 | 0.3244 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8897 | +| 0.2785 | 0.6517 | 2530 | 0.3348 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8874 | +| 0.3362 | 0.6530 | 2535 | 0.3436 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8871 | +| 0.3864 | 0.6543 | 2540 | 0.3411 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8858 | +| 0.3527 | 0.6556 | 2545 | 0.3303 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8819 | +| 0.3117 | 0.6569 | 2550 | 0.3317 | 0.8585 | 0.8953 | 0.9277 | 0.6087 | 0.7 | 0.9112 | 0.6512 | 0.7682 | 0.8811 | +| 0.2775 | 0.6582 | 2555 | 0.3315 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8832 | +| 0.3132 | 0.6594 | 2560 | 0.3460 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8832 | +| 0.2851 | 0.6607 | 2565 | 0.3255 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8871 | +| 0.3449 | 0.6620 | 2570 | 0.3227 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8861 | +| 0.3432 | 0.6633 | 2575 | 0.3256 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8897 | +| 0.3479 | 0.6646 | 2580 | 0.3271 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8874 | +| 0.3447 | 0.6659 | 2585 | 0.3356 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8858 | +| 0.3198 | 0.6672 | 2590 | 0.3364 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8910 | +| 0.3317 | 0.6685 | 2595 | 0.3372 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8916 | +| 0.2696 | 0.6697 | 2600 | 0.3448 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8939 | +| 0.3551 | 0.6710 | 2605 | 0.3303 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8965 | +| 0.3389 | 0.6723 | 2610 | 0.3231 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8955 | +| 0.3811 | 0.6736 | 2615 | 0.3184 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8931 | +| 0.2939 | 0.6749 | 2620 | 0.3204 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8926 | +| 0.3233 | 0.6762 | 2625 | 0.3313 | 0.8679 | 0.8791 | 0.9639 | 0.5217 | 0.8 | 0.9195 | 0.6316 | 0.7428 | 0.8944 | +| 0.2598 | 0.6775 | 2630 | 0.3367 | 0.8585 | 0.8778 | 0.9518 | 0.5217 | 0.75 | 0.9133 | 0.6154 | 0.7368 | 0.8897 | +| 0.2962 | 0.6788 | 2635 | 0.3350 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8892 | +| 0.3069 | 0.6801 | 2640 | 0.3217 | 0.8396 | 0.875 | 0.9277 | 0.5217 | 0.6667 | 0.9006 | 0.5854 | 0.7247 | 0.8866 | +| 0.2945 | 0.6813 | 2645 | 0.3209 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8871 | +| 0.325 | 0.6826 | 2650 | 0.3145 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8931 | +| 0.3437 | 0.6839 | 2655 | 0.3072 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8905 | +| 0.3764 | 0.6852 | 2660 | 0.3084 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8999 | +| 0.3309 | 0.6865 | 2665 | 0.3094 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.9031 | +| 0.287 | 0.6878 | 2670 | 0.3078 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.9034 | +| 0.314 | 0.6891 | 2675 | 0.2978 | 0.8491 | 0.8941 | 0.9157 | 0.6087 | 0.6667 | 0.9048 | 0.6364 | 0.7622 | 0.9044 | +| 0.2762 | 0.6904 | 2680 | 0.2989 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.9047 | +| 0.2767 | 0.6916 | 2685 | 0.3013 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.9057 | +| 0.3169 | 0.6929 | 2690 | 0.3011 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.9034 | +| 0.3732 | 0.6942 | 2695 | 0.3036 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.9044 | +| 0.3397 | 0.6955 | 2700 | 0.3038 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.9023 | +| 0.3578 | 0.6968 | 2705 | 0.3036 | 0.8585 | 0.8953 | 0.9277 | 0.6087 | 0.7 | 0.9112 | 0.6512 | 0.7682 | 0.9015 | +| 0.3601 | 0.6981 | 2710 | 0.3020 | 0.8585 | 0.8953 | 0.9277 | 0.6087 | 0.7 | 0.9112 | 0.6512 | 0.7682 | 0.9013 | +| 0.379 | 0.6994 | 2715 | 0.3085 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.9010 | +| 0.3188 | 0.7007 | 2720 | 0.3102 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8981 | +| 0.3211 | 0.7019 | 2725 | 0.3142 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8944 | +| 0.2895 | 0.7032 | 2730 | 0.3210 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8889 | +| 0.3349 | 0.7045 | 2735 | 0.3168 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8827 | +| 0.3331 | 0.7058 | 2740 | 0.3197 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8774 | +| 0.3342 | 0.7071 | 2745 | 0.3299 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8800 | +| 0.357 | 0.7084 | 2750 | 0.3353 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8795 | +| 0.2873 | 0.7097 | 2755 | 0.3327 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8790 | +| 0.3098 | 0.7110 | 2760 | 0.3285 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8766 | +| 0.377 | 0.7123 | 2765 | 0.3243 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8785 | +| 0.3254 | 0.7135 | 2770 | 0.3213 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8774 | +| 0.2932 | 0.7148 | 2775 | 0.3272 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8782 | +| 0.2667 | 0.7161 | 2780 | 0.3318 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8800 | +| 0.2902 | 0.7174 | 2785 | 0.3448 | 0.8774 | 0.8889 | 0.9639 | 0.5652 | 0.8125 | 0.9249 | 0.6667 | 0.7645 | 0.8866 | +| 0.3304 | 0.7187 | 2790 | 0.3504 | 0.8774 | 0.8889 | 0.9639 | 0.5652 | 0.8125 | 0.9249 | 0.6667 | 0.7645 | 0.8895 | +| 0.2649 | 0.7200 | 2795 | 0.3322 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8850 | +| 0.4551 | 0.7213 | 2800 | 0.3191 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8861 | +| 0.3036 | 0.7226 | 2805 | 0.3169 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8832 | +| 0.3181 | 0.7238 | 2810 | 0.3173 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8840 | +| 0.3811 | 0.7251 | 2815 | 0.3163 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8832 | +| 0.3117 | 0.7264 | 2820 | 0.3231 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8848 | +| 0.2588 | 0.7277 | 2825 | 0.3275 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8908 | +| 0.3206 | 0.7290 | 2830 | 0.3278 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8876 | +| 0.2556 | 0.7303 | 2835 | 0.3247 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8924 | +| 0.363 | 0.7316 | 2840 | 0.3263 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8905 | +| 0.3477 | 0.7329 | 2845 | 0.3257 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8937 | +| 0.4682 | 0.7341 | 2850 | 0.3281 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8924 | +| 0.3298 | 0.7354 | 2855 | 0.3247 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8955 | +| 0.4312 | 0.7367 | 2860 | 0.3182 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8944 | +| 0.2243 | 0.7380 | 2865 | 0.3202 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8908 | +| 0.3566 | 0.7393 | 2870 | 0.3267 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8913 | +| 0.3565 | 0.7406 | 2875 | 0.3372 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8882 | +| 0.3053 | 0.7419 | 2880 | 0.3382 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8900 | +| 0.3184 | 0.7432 | 2885 | 0.3290 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8905 | +| 0.355 | 0.7444 | 2890 | 0.3244 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8887 | +| 0.4111 | 0.7457 | 2895 | 0.3182 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8900 | +| 0.3215 | 0.7470 | 2900 | 0.3174 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8905 | +| 0.4032 | 0.7483 | 2905 | 0.3181 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8916 | +| 0.3235 | 0.7496 | 2910 | 0.3241 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8918 | +| 0.2664 | 0.7509 | 2915 | 0.3280 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8950 | +| 0.3667 | 0.7522 | 2920 | 0.3353 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8937 | +| 0.354 | 0.7535 | 2925 | 0.3332 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8892 | +| 0.3875 | 0.7548 | 2930 | 0.3289 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8892 | +| 0.3068 | 0.7560 | 2935 | 0.3265 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8869 | +| 0.2951 | 0.7573 | 2940 | 0.3272 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8850 | +| 0.296 | 0.7586 | 2945 | 0.3321 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8848 | +| 0.3036 | 0.7599 | 2950 | 0.3356 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8855 | +| 0.2838 | 0.7612 | 2955 | 0.3349 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8845 | +| 0.3022 | 0.7625 | 2960 | 0.3344 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8834 | +| 0.3094 | 0.7638 | 2965 | 0.3360 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8816 | +| 0.3199 | 0.7651 | 2970 | 0.3381 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8850 | +| 0.3841 | 0.7663 | 2975 | 0.3449 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8827 | +| 0.4193 | 0.7676 | 2980 | 0.3567 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8814 | +| 0.341 | 0.7689 | 2985 | 0.3625 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8811 | +| 0.3551 | 0.7702 | 2990 | 0.3676 | 0.8585 | 0.8778 | 0.9518 | 0.5217 | 0.75 | 0.9133 | 0.6154 | 0.7368 | 0.8803 | +| 0.294 | 0.7715 | 2995 | 0.3736 | 0.8585 | 0.8778 | 0.9518 | 0.5217 | 0.75 | 0.9133 | 0.6154 | 0.7368 | 0.8819 | +| 0.3366 | 0.7728 | 3000 | 0.3652 | 0.8491 | 0.8764 | 0.9398 | 0.5217 | 0.7059 | 0.9070 | 0.6 | 0.7307 | 0.8787 | +| 0.2738 | 0.7741 | 3005 | 0.3482 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8819 | +| 0.4343 | 0.7754 | 3010 | 0.3433 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8782 | +| 0.3214 | 0.7766 | 3015 | 0.3396 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8803 | +| 0.2924 | 0.7779 | 3020 | 0.3349 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8808 | +| 0.371 | 0.7792 | 3025 | 0.3374 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8821 | +| 0.3128 | 0.7805 | 3030 | 0.3386 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8863 | +| 0.3587 | 0.7818 | 3035 | 0.3404 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8855 | +| 0.3105 | 0.7831 | 3040 | 0.3363 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8863 | +| 0.3886 | 0.7844 | 3045 | 0.3309 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8821 | +| 0.3189 | 0.7857 | 3050 | 0.3292 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8840 | +| 0.3174 | 0.7870 | 3055 | 0.3319 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8850 | +| 0.358 | 0.7882 | 3060 | 0.3348 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8842 | +| 0.28 | 0.7895 | 3065 | 0.3338 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8850 | +| 0.3364 | 0.7908 | 3070 | 0.3294 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8840 | +| 0.2429 | 0.7921 | 3075 | 0.3262 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8850 | +| 0.3022 | 0.7934 | 3080 | 0.3253 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8855 | +| 0.3441 | 0.7947 | 3085 | 0.3257 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8842 | +| 0.287 | 0.7960 | 3090 | 0.3277 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8840 | +| 0.3424 | 0.7973 | 3095 | 0.3302 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8834 | +| 0.3639 | 0.7985 | 3100 | 0.3361 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8819 | +| 0.2834 | 0.7998 | 3105 | 0.3378 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8853 | +| 0.3266 | 0.8011 | 3110 | 0.3420 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8845 | +| 0.3232 | 0.8024 | 3115 | 0.3464 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8819 | +| 0.2542 | 0.8037 | 3120 | 0.3414 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8806 | +| 0.2871 | 0.8050 | 3125 | 0.3344 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8821 | +| 0.2927 | 0.8063 | 3130 | 0.3306 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8793 | +| 0.3634 | 0.8076 | 3135 | 0.3306 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8779 | +| 0.5215 | 0.8088 | 3140 | 0.3298 | 0.8208 | 0.8810 | 0.8916 | 0.5652 | 0.5909 | 0.8862 | 0.5778 | 0.7284 | 0.8777 | +| 0.3614 | 0.8101 | 3145 | 0.3326 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8774 | +| 0.4001 | 0.8114 | 3150 | 0.3320 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8814 | +| 0.3338 | 0.8127 | 3155 | 0.3339 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8832 | +| 0.3734 | 0.8140 | 3160 | 0.3338 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8848 | +| 0.3096 | 0.8153 | 3165 | 0.3343 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8824 | +| 0.3147 | 0.8166 | 3170 | 0.3353 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8879 | +| 0.2613 | 0.8179 | 3175 | 0.3372 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8871 | +| 0.2922 | 0.8192 | 3180 | 0.3329 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8858 | +| 0.4042 | 0.8204 | 3185 | 0.3287 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8871 | +| 0.3299 | 0.8217 | 3190 | 0.3251 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8874 | +| 0.407 | 0.8230 | 3195 | 0.3250 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8874 | +| 0.3315 | 0.8243 | 3200 | 0.3233 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8855 | +| 0.3402 | 0.8256 | 3205 | 0.3228 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8842 | +| 0.2778 | 0.8269 | 3210 | 0.3223 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8871 | +| 0.245 | 0.8282 | 3215 | 0.3251 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8845 | +| 0.3066 | 0.8295 | 3220 | 0.3265 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8876 | +| 0.3827 | 0.8307 | 3225 | 0.3333 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8845 | +| 0.3336 | 0.8320 | 3230 | 0.3358 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8814 | +| 0.3671 | 0.8333 | 3235 | 0.3370 | 0.8585 | 0.8864 | 0.9398 | 0.5652 | 0.7222 | 0.9123 | 0.6341 | 0.7525 | 0.8816 | +| 0.2703 | 0.8346 | 3240 | 0.3409 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8800 | +| 0.2795 | 0.8359 | 3245 | 0.3433 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8800 | +| 0.3502 | 0.8372 | 3250 | 0.3462 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8821 | +| 0.2922 | 0.8385 | 3255 | 0.3465 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8772 | +| 0.374 | 0.8398 | 3260 | 0.3442 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8785 | +| 0.2706 | 0.8410 | 3265 | 0.3412 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8785 | +| 0.2961 | 0.8423 | 3270 | 0.3406 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8790 | +| 0.3422 | 0.8436 | 3275 | 0.3406 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8772 | +| 0.2893 | 0.8449 | 3280 | 0.3395 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8785 | +| 0.3699 | 0.8462 | 3285 | 0.3358 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8782 | +| 0.3141 | 0.8475 | 3290 | 0.3365 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8800 | +| 0.3418 | 0.8488 | 3295 | 0.3338 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8811 | +| 0.2966 | 0.8501 | 3300 | 0.3344 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8790 | +| 0.2757 | 0.8514 | 3305 | 0.3351 | 0.8302 | 0.8824 | 0.9036 | 0.5652 | 0.6190 | 0.8929 | 0.5909 | 0.7344 | 0.8819 | +| 0.3715 | 0.8526 | 3310 | 0.3378 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8837 | +| 0.2946 | 0.8539 | 3315 | 0.3399 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8811 | +| 0.2893 | 0.8552 | 3320 | 0.3392 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8816 | +| 0.3125 | 0.8565 | 3325 | 0.3412 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8798 | +| 0.3769 | 0.8578 | 3330 | 0.3402 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8814 | +| 0.3503 | 0.8591 | 3335 | 0.3392 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8808 | +| 0.2575 | 0.8604 | 3340 | 0.3401 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8858 | +| 0.3653 | 0.8617 | 3345 | 0.3417 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8821 | +| 0.2973 | 0.8629 | 3350 | 0.3439 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8829 | +| 0.2872 | 0.8642 | 3355 | 0.3419 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8811 | +| 0.251 | 0.8655 | 3360 | 0.3405 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8827 | +| 0.377 | 0.8668 | 3365 | 0.3408 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8827 | +| 0.3355 | 0.8681 | 3370 | 0.3373 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8803 | +| 0.3121 | 0.8694 | 3375 | 0.3338 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8811 | +| 0.3279 | 0.8707 | 3380 | 0.3349 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8803 | +| 0.3237 | 0.8720 | 3385 | 0.3355 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8779 | +| 0.3347 | 0.8732 | 3390 | 0.3367 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8790 | +| 0.2821 | 0.8745 | 3395 | 0.3422 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8795 | +| 0.2977 | 0.8758 | 3400 | 0.3436 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8821 | +| 0.3077 | 0.8771 | 3405 | 0.3426 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8793 | +| 0.3448 | 0.8784 | 3410 | 0.3433 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8814 | +| 0.2824 | 0.8797 | 3415 | 0.3386 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8814 | +| 0.3484 | 0.8810 | 3420 | 0.3360 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8821 | +| 0.2562 | 0.8823 | 3425 | 0.3342 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8790 | +| 0.3337 | 0.8836 | 3430 | 0.3372 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8795 | +| 0.3367 | 0.8848 | 3435 | 0.3340 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8819 | +| 0.4324 | 0.8861 | 3440 | 0.3373 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8834 | +| 0.3044 | 0.8874 | 3445 | 0.3396 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8819 | +| 0.316 | 0.8887 | 3450 | 0.3392 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8806 | +| 0.3222 | 0.8900 | 3455 | 0.3362 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8850 | +| 0.3655 | 0.8913 | 3460 | 0.3419 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8824 | +| 0.3393 | 0.8926 | 3465 | 0.3385 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8848 | +| 0.278 | 0.8939 | 3470 | 0.3363 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8848 | +| 0.3475 | 0.8951 | 3475 | 0.3374 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8861 | +| 0.263 | 0.8964 | 3480 | 0.3376 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8848 | +| 0.3656 | 0.8977 | 3485 | 0.3364 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8874 | +| 0.3449 | 0.8990 | 3490 | 0.3353 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8858 | +| 0.2766 | 0.9003 | 3495 | 0.3365 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8842 | +| 0.3439 | 0.9016 | 3500 | 0.3333 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8848 | +| 0.3697 | 0.9029 | 3505 | 0.3346 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8848 | +| 0.3781 | 0.9042 | 3510 | 0.3337 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8861 | +| 0.3978 | 0.9054 | 3515 | 0.3340 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8858 | +| 0.3177 | 0.9067 | 3520 | 0.3346 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8832 | +| 0.3766 | 0.9080 | 3525 | 0.3321 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8869 | +| 0.2556 | 0.9093 | 3530 | 0.3324 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8858 | +| 0.3714 | 0.9106 | 3535 | 0.3334 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8861 | +| 0.425 | 0.9119 | 3540 | 0.3322 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8855 | +| 0.2542 | 0.9132 | 3545 | 0.3300 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8882 | +| 0.3575 | 0.9145 | 3550 | 0.3295 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8861 | +| 0.4028 | 0.9158 | 3555 | 0.3289 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8869 | +| 0.3116 | 0.9170 | 3560 | 0.3309 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8858 | +| 0.3175 | 0.9183 | 3565 | 0.3312 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8861 | +| 0.3083 | 0.9196 | 3570 | 0.3306 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8863 | +| 0.3399 | 0.9209 | 3575 | 0.3327 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8863 | +| 0.3876 | 0.9222 | 3580 | 0.3317 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8874 | +| 0.2752 | 0.9235 | 3585 | 0.3325 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8850 | +| 0.2981 | 0.9248 | 3590 | 0.3324 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8848 | +| 0.2946 | 0.9261 | 3595 | 0.3310 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8874 | +| 0.3568 | 0.9273 | 3600 | 0.3301 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8855 | +| 0.3326 | 0.9286 | 3605 | 0.3306 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8858 | +| 0.3633 | 0.9299 | 3610 | 0.3310 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8858 | +| 0.3136 | 0.9312 | 3615 | 0.3311 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8869 | +| 0.2585 | 0.9325 | 3620 | 0.3293 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8866 | +| 0.3148 | 0.9338 | 3625 | 0.3315 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8866 | +| 0.3251 | 0.9351 | 3630 | 0.3305 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8887 | +| 0.2887 | 0.9364 | 3635 | 0.3325 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8884 | +| 0.3785 | 0.9376 | 3640 | 0.3303 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8874 | +| 0.3284 | 0.9389 | 3645 | 0.3319 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8871 | +| 0.3662 | 0.9402 | 3650 | 0.3281 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8882 | +| 0.3163 | 0.9415 | 3655 | 0.3328 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8853 | +| 0.4627 | 0.9428 | 3660 | 0.3298 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8887 | +| 0.398 | 0.9441 | 3665 | 0.3338 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8863 | +| 0.2672 | 0.9454 | 3670 | 0.3306 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8869 | +| 0.4093 | 0.9467 | 3675 | 0.3309 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8879 | +| 0.3645 | 0.9479 | 3680 | 0.3315 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8863 | +| 0.2788 | 0.9492 | 3685 | 0.3296 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8866 | +| 0.3817 | 0.9505 | 3690 | 0.3314 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8871 | +| 0.2894 | 0.9518 | 3695 | 0.3315 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8863 | +| 0.2894 | 0.9531 | 3700 | 0.3317 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8869 | +| 0.3176 | 0.9544 | 3705 | 0.3291 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8874 | +| 0.3114 | 0.9557 | 3710 | 0.3309 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8874 | +| 0.3834 | 0.9570 | 3715 | 0.3281 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8869 | +| 0.3931 | 0.9583 | 3720 | 0.3290 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8874 | +| 0.311 | 0.9595 | 3725 | 0.3281 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8869 | +| 0.3791 | 0.9608 | 3730 | 0.3297 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8887 | +| 0.3646 | 0.9621 | 3735 | 0.3293 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8853 | +| 0.4257 | 0.9634 | 3740 | 0.3300 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8882 | +| 0.2983 | 0.9647 | 3745 | 0.3308 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8879 | +| 0.283 | 0.9660 | 3750 | 0.3319 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8884 | +| 0.2856 | 0.9673 | 3755 | 0.3296 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8887 | +| 0.3199 | 0.9686 | 3760 | 0.3337 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8858 | +| 0.3381 | 0.9698 | 3765 | 0.3321 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8855 | +| 0.3557 | 0.9711 | 3770 | 0.3307 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8879 | +| 0.3118 | 0.9724 | 3775 | 0.3307 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8879 | +| 0.375 | 0.9737 | 3780 | 0.3300 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8882 | +| 0.2593 | 0.9750 | 3785 | 0.3323 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8887 | +| 0.3242 | 0.9763 | 3790 | 0.3322 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8863 | +| 0.3827 | 0.9776 | 3795 | 0.3310 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8874 | +| 0.3279 | 0.9789 | 3800 | 0.3319 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8879 | +| 0.2307 | 0.9801 | 3805 | 0.3311 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8869 | +| 0.333 | 0.9814 | 3810 | 0.3325 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8876 | +| 0.3546 | 0.9827 | 3815 | 0.3323 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8876 | +| 0.2873 | 0.9840 | 3820 | 0.3321 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8874 | +| 0.4467 | 0.9853 | 3825 | 0.3331 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8850 | +| 0.3029 | 0.9866 | 3830 | 0.3327 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8879 | +| 0.3706 | 0.9879 | 3835 | 0.3324 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8876 | +| 0.2942 | 0.9892 | 3840 | 0.3319 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8855 | +| 0.3261 | 0.9905 | 3845 | 0.3322 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8871 | +| 0.3903 | 0.9917 | 3850 | 0.3330 | 0.8396 | 0.8837 | 0.9157 | 0.5652 | 0.65 | 0.8994 | 0.6047 | 0.7404 | 0.8855 | +| 0.2563 | 0.9930 | 3855 | 0.3319 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8882 | +| 0.3745 | 0.9943 | 3860 | 0.3333 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8874 | +| 0.2862 | 0.9956 | 3865 | 0.3323 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8858 | +| 0.3274 | 0.9969 | 3870 | 0.3324 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8869 | +| 0.2292 | 0.9982 | 3875 | 0.3321 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8874 | +| 0.3212 | 0.9995 | 3880 | 0.3303 | 0.8491 | 0.8851 | 0.9277 | 0.5652 | 0.6842 | 0.9059 | 0.6190 | 0.7465 | 0.8876 | + + +### Framework versions + +- Transformers 4.46.0 +- Pytorch 2.4.0+cu118 +- Datasets 3.0.0 +- Tokenizers 0.20.1 diff --git a/config.json b/config.json new file mode 100644 index 0000000..0b0b35a --- /dev/null +++ b/config.json @@ -0,0 +1,40 @@ +{ + "_name_or_path": "meta-llama/Llama-3.1-8B-Instruct", + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": [ + 128001, + 128008, + 128009 + ], + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 131072, + "mlp_bias": false, + "model_type": "llama", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pretraining_tp": 1, + "rms_norm_eps": 1e-05, + "rope_scaling": { + "factor": 8.0, + "high_freq_factor": 4.0, + "low_freq_factor": 1.0, + "original_max_position_embeddings": 8192, + "rope_type": "llama3" + }, + "rope_theta": 500000.0, + "tie_word_embeddings": false, + "torch_dtype": "bfloat16", + "transformers_version": "4.46.0", + "use_cache": true, + "vocab_size": 128256 +} diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..85636bf --- /dev/null +++ b/generation_config.json @@ -0,0 +1,12 @@ +{ + "bos_token_id": 128000, + "do_sample": true, + "eos_token_id": [ + 128001, + 128008, + 128009 + ], + "temperature": 0.6, + "top_p": 0.9, + "transformers_version": "4.46.0" +} diff --git a/last-checkpoint/config.json b/last-checkpoint/config.json new file mode 100644 index 0000000..0b0b35a --- /dev/null +++ b/last-checkpoint/config.json @@ -0,0 +1,40 @@ +{ + "_name_or_path": "meta-llama/Llama-3.1-8B-Instruct", + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": [ + 128001, + 128008, + 128009 + ], + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 4096, + "initializer_range": 0.02, + "intermediate_size": 14336, + "max_position_embeddings": 131072, + "mlp_bias": false, + "model_type": "llama", + "num_attention_heads": 32, + "num_hidden_layers": 32, + "num_key_value_heads": 8, + "pretraining_tp": 1, + "rms_norm_eps": 1e-05, + "rope_scaling": { + "factor": 8.0, + "high_freq_factor": 4.0, + "low_freq_factor": 1.0, + "original_max_position_embeddings": 8192, + "rope_type": "llama3" + 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"num_input_tokens_seen": 0, + "num_train_epochs": 1, + "save_steps": 500, + "stateful_callbacks": { + "TrainerControl": { + "args": { + "should_epoch_stop": false, + "should_evaluate": false, + "should_log": false, + "should_save": true, + "should_training_stop": false + }, + "attributes": {} + } + }, + "total_flos": 177649478123520.0, + "train_batch_size": 1, + "trial_name": null, + "trial_params": null +} diff --git a/last-checkpoint/training_args.bin b/last-checkpoint/training_args.bin new file mode 100644 index 0000000..df6122e --- /dev/null +++ b/last-checkpoint/training_args.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3499d775f2c2601ebb70f05a8a397ab1e33c7dd2c1a7c9d334a8f1f6462ed367 +size 6520 diff --git a/last-checkpoint/zero_to_fp32.py b/last-checkpoint/zero_to_fp32.py new file mode 100644 index 0000000..e69ecd9 --- /dev/null +++ b/last-checkpoint/zero_to_fp32.py @@ -0,0 +1,674 @@ +#!/usr/bin/env python + +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets +# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in +# the future. Once extracted, the weights don't require DeepSpeed and can be used in any +# application. +# +# example: +# python zero_to_fp32.py . output_dir/ +# or +# python zero_to_fp32.py . output_dir/ --safe_serialization + +import argparse +import torch +import glob +import math +import os +import re +import json +from tqdm import tqdm +from collections import OrderedDict +from dataclasses import dataclass + +# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with +# DeepSpeed data structures it has to be available in the current python environment. +from deepspeed.utils import logger +from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS, + FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES, + FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS) + + +@dataclass +class zero_model_state: + buffers: dict() + param_shapes: dict() + shared_params: list + ds_version: int + frozen_param_shapes: dict() + frozen_param_fragments: dict() + + +debug = 0 + +# load to cpu +device = torch.device('cpu') + + +def atoi(text): + return int(text) if text.isdigit() else text + + +def natural_keys(text): + ''' + alist.sort(key=natural_keys) sorts in human order + http://nedbatchelder.com/blog/200712/human_sorting.html + (See Toothy's implementation in the comments) + ''' + return [atoi(c) for c in re.split(r'(\d+)', text)] + + +def get_model_state_file(checkpoint_dir, zero_stage): + if not os.path.isdir(checkpoint_dir): + raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist") + + # there should be only one file + if zero_stage <= 2: + file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt") + elif zero_stage == 3: + file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt") + + if not os.path.exists(file): + raise FileNotFoundError(f"can't find model states file at '{file}'") + + return file + + +def get_checkpoint_files(checkpoint_dir, glob_pattern): + # XXX: need to test that this simple glob rule works for multi-node setup too + ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys) + + if len(ckpt_files) == 0: + raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'") + + return ckpt_files + + +def get_optim_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt") + + +def get_model_state_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_model_states.pt") + + +def parse_model_states(files): + zero_model_states = [] + for file in files: + state_dict = torch.load(file, map_location=device) + + if BUFFER_NAMES not in state_dict: + raise ValueError(f"{file} is not a model state checkpoint") + buffer_names = state_dict[BUFFER_NAMES] + if debug: + print("Found buffers:", buffer_names) + + # recover just the buffers while restoring them to fp32 if they were saved in fp16 + buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names} + param_shapes = state_dict[PARAM_SHAPES] + + # collect parameters that are included in param_shapes + param_names = [] + for s in param_shapes: + for name in s.keys(): + param_names.append(name) + + # update with frozen parameters + frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None) + if frozen_param_shapes is not None: + if debug: + print(f"Found frozen_param_shapes: {frozen_param_shapes}") + param_names += list(frozen_param_shapes.keys()) + + # handle shared params + shared_params = [[k, v] for k, v in state_dict["shared_params"].items()] + + ds_version = state_dict.get(DS_VERSION, None) + + frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None) + + z_model_state = zero_model_state(buffers=buffers, + param_shapes=param_shapes, + shared_params=shared_params, + ds_version=ds_version, + frozen_param_shapes=frozen_param_shapes, + frozen_param_fragments=frozen_param_fragments) + zero_model_states.append(z_model_state) + + return zero_model_states + + +def parse_optim_states(files, ds_checkpoint_dir): + total_files = len(files) + state_dicts = [] + for f in files: + state_dict = torch.load(f, map_location=device) + # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights + # and also handle the case where it was already removed by another helper script + state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None) + state_dicts.append(state_dict) + + if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]: + raise ValueError(f"{files[0]} is not a zero checkpoint") + zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE] + world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT] + + # For ZeRO-2 each param group can have different partition_count as data parallelism for expert + # parameters can be different from data parallelism for non-expert parameters. So we can just + # use the max of the partition_count to get the dp world_size. + + if type(world_size) is list: + world_size = max(world_size) + + if world_size != total_files: + raise ValueError( + f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. " + "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes." + ) + + # the groups are named differently in each stage + if zero_stage <= 2: + fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS + elif zero_stage == 3: + fp32_groups_key = FP32_FLAT_GROUPS + else: + raise ValueError(f"unknown zero stage {zero_stage}") + + if zero_stage <= 2: + fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))] + elif zero_stage == 3: + # if there is more than one param group, there will be multiple flattened tensors - one + # flattened tensor per group - for simplicity merge them into a single tensor + # + # XXX: could make the script more memory efficient for when there are multiple groups - it + # will require matching the sub-lists of param_shapes for each param group flattened tensor + + fp32_flat_groups = [ + torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts)) + ] + + return zero_stage, world_size, fp32_flat_groups + + +def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters): + """ + Returns fp32 state_dict reconstructed from ds checkpoint + + Args: + - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are) + + """ + print(f"Processing zero checkpoint '{ds_checkpoint_dir}'") + + optim_files = get_optim_files(ds_checkpoint_dir) + zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir) + print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}") + + model_files = get_model_state_files(ds_checkpoint_dir) + + zero_model_states = parse_model_states(model_files) + print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}') + + if zero_stage <= 2: + return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters) + elif zero_stage == 3: + return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters) + + +def _zero2_merge_frozen_params(state_dict, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + frozen_param_fragments = zero_model_states[0].frozen_param_fragments + + if debug: + num_elem = sum(s.numel() for s in frozen_param_shapes.values()) + print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in frozen_param_fragments.values()]) + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + state_dict[name] = frozen_param_fragments[name] + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +def _has_callable(obj, fn): + attr = getattr(obj, fn, None) + return callable(attr) + + +def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + + # Reconstruction protocol: + # + # XXX: document this + + if debug: + for i in range(world_size): + for j in range(len(fp32_flat_groups[0])): + print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}") + + # XXX: memory usage doubles here (zero2) + num_param_groups = len(fp32_flat_groups[0]) + merged_single_partition_of_fp32_groups = [] + for i in range(num_param_groups): + merged_partitions = [sd[i] for sd in fp32_flat_groups] + full_single_fp32_vector = torch.cat(merged_partitions, 0) + merged_single_partition_of_fp32_groups.append(full_single_fp32_vector) + avail_numel = sum( + [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups]) + + if debug: + wanted_params = sum([len(shapes) for shapes in param_shapes]) + wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes]) + # not asserting if there is a mismatch due to possible padding + print(f"Have {avail_numel} numels to process.") + print(f"Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + total_numel = 0 + total_params = 0 + for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups): + offset = 0 + avail_numel = full_single_fp32_vector.numel() + for name, shape in shapes.items(): + + unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape) + total_numel += unpartitioned_numel + total_params += 1 + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape) + offset += unpartitioned_numel + + # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and + # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex + # paddings performed in the code it's almost impossible to predict the exact numbers w/o the + # live optimizer object, so we are checking that the numbers are within the right range + align_to = 2 * world_size + + def zero2_align(x): + return align_to * math.ceil(x / align_to) + + if debug: + print(f"original offset={offset}, avail_numel={avail_numel}") + + offset = zero2_align(offset) + avail_numel = zero2_align(avail_numel) + + if debug: + print(f"aligned offset={offset}, avail_numel={avail_numel}") + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + if not exclude_frozen_parameters: + _zero2_merge_frozen_params(state_dict, zero_model_states) + + _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def zero3_partitioned_param_info(unpartitioned_numel, world_size): + remainder = unpartitioned_numel % world_size + padding_numel = (world_size - remainder) if remainder else 0 + partitioned_numel = math.ceil(unpartitioned_numel / world_size) + return partitioned_numel, padding_numel + + +def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + if debug: + for i in range(world_size): + num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values()) + print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in zero_model_states[0].frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states) + state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape) + + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + avail_numel = fp32_flat_groups[0].numel() * world_size + # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each + # param, re-consolidating each param, while dealing with padding if any + + # merge list of dicts, preserving order + param_shapes = {k: v for d in param_shapes for k, v in d.items()} + + if debug: + for i in range(world_size): + print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}") + + wanted_params = len(param_shapes) + wanted_numel = sum(shape.numel() for shape in param_shapes.values()) + # not asserting if there is a mismatch due to possible padding + avail_numel = fp32_flat_groups[0].numel() * world_size + print(f"Trainable params: Have {avail_numel} numels to process.") + print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + offset = 0 + total_numel = 0 + total_params = 0 + for name, shape in tqdm(param_shapes.items(), desc='Gathering Sharded Weights'): + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + total_params += 1 + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + # XXX: memory usage doubles here + state_dict[name] = torch.cat( + tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)), + 0).narrow(0, 0, unpartitioned_numel).view(shape) + offset += partitioned_numel + + offset *= world_size + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states, + exclude_frozen_parameters): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + if not exclude_frozen_parameters: + _zero3_merge_frozen_params(state_dict, world_size, zero_model_states) + + _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None, exclude_frozen_parameters=False): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with + ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example + via a model hub. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14`` + - ``exclude_frozen_parameters``: exclude frozen parameters + + Returns: + - pytorch ``state_dict`` + + Note: this approach may not work if your application doesn't have sufficient free CPU memory and + you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with + the checkpoint. + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + # do the training and checkpoint saving + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu + model = model.cpu() # move to cpu + model.load_state_dict(state_dict) + # submit to model hub or save the model to share with others + + In this example the ``model`` will no longer be usable in the deepspeed context of the same + application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead. + + """ + if tag is None: + latest_path = os.path.join(checkpoint_dir, 'latest') + if os.path.isfile(latest_path): + with open(latest_path, 'r') as fd: + tag = fd.read().strip() + else: + raise ValueError(f"Unable to find 'latest' file at {latest_path}") + + ds_checkpoint_dir = os.path.join(checkpoint_dir, tag) + + if not os.path.isdir(ds_checkpoint_dir): + raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist") + + return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters) + + +def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, + output_dir, + max_shard_size="5GB", + safe_serialization=False, + tag=None, + exclude_frozen_parameters=False): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be + loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``output_dir``: directory to the pytorch fp32 state_dict output files + - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB + - ``safe_serialization``: whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`). + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + - ``exclude_frozen_parameters``: exclude frozen parameters + """ + # Dependency pre-check + if safe_serialization: + try: + from safetensors.torch import save_file + except ImportError: + print('If you want to use `safe_serialization`, please `pip install safetensors`') + raise + if max_shard_size is not None: + try: + from huggingface_hub import split_torch_state_dict_into_shards + except ImportError: + print('If you want to use `max_shard_size`, please `pip install huggingface_hub`') + raise + + # Convert zero checkpoint to state_dict + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag, exclude_frozen_parameters) + + # Shard the model if it is too big. + weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin" + if max_shard_size is not None: + filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors") + state_dict_split = split_torch_state_dict_into_shards(state_dict, + filename_pattern=filename_pattern, + max_shard_size=max_shard_size) + else: + from collections import namedtuple + StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"]) + state_dict_split = StateDictSplit(is_sharded=False, + filename_to_tensors={weights_name: list(state_dict.keys())}) + + # Save the model + filename_to_tensors = state_dict_split.filename_to_tensors.items() + for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"): + shard = {tensor: state_dict[tensor].contiguous() for tensor in tensors} + output_path = os.path.join(output_dir, shard_file) + if safe_serialization: + save_file(shard, output_path, metadata={"format": "pt"}) + else: + torch.save(shard, output_path) + + # Save index if sharded + if state_dict_split.is_sharded: + index = { + "metadata": state_dict_split.metadata, + "weight_map": state_dict_split.tensor_to_filename, + } + save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json" + save_index_file = os.path.join(output_dir, save_index_file) + with open(save_index_file, "w", encoding="utf-8") as f: + content = json.dumps(index, indent=2, sort_keys=True) + "\n" + f.write(content) + + +def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None): + """ + 1. Put the provided model to cpu + 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` + 3. Load it into the provided model + + Args: + - ``model``: the model object to update + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + + Returns: + - ``model`: modified model + + Make sure you have plenty of CPU memory available before you call this function. If you don't + have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it + conveniently placed for you in the checkpoint folder. + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint + model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir) + # submit to model hub or save the model to share with others + + Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context + of the same application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + """ + logger.info(f"Extracting fp32 weights") + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag) + + logger.info(f"Overwriting model with fp32 weights") + model = model.cpu() + model.load_state_dict(state_dict, strict=False) + + return model + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("checkpoint_dir", + type=str, + help="path to the desired checkpoint folder, e.g., path/checkpoint-12") + parser.add_argument("output_dir", + type=str, + help="directory to the pytorch fp32 state_dict output files" + "(e.g. path/checkpoint-12-output/)") + parser.add_argument( + "--max_shard_size", + type=str, + default="5GB", + help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size" + "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`" + "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances" + "without CPU OOM issues.") + parser.add_argument( + "--safe_serialization", + default=False, + action='store_true', + help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).") + parser.add_argument("-t", + "--tag", + type=str, + default=None, + help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1") + parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters") + parser.add_argument("-d", "--debug", action='store_true', help="enable debug") + args = parser.parse_args() + + debug = args.debug + + convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, + args.output_dir, + max_shard_size=args.max_shard_size, + safe_serialization=args.safe_serialization, + tag=args.tag, + 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tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n", + "clean_up_tokenization_spaces": true, + "eos_token": "<|eot_id|>", + "model_input_names": [ + "input_ids", + "attention_mask" + ], + "model_max_length": 131072, + "pad_token": "<|eot_id|>", + "tokenizer_class": "PreTrainedTokenizerFast" +} diff --git a/training_args.bin b/training_args.bin new file mode 100644 index 0000000..df6122e --- /dev/null +++ b/training_args.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3499d775f2c2601ebb70f05a8a397ab1e33c7dd2c1a7c9d334a8f1f6462ed367 +size 6520