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vit-SUPER02

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  2. runs/Feb03_03-32-21_c46e18eeb47a/events.out.tfevents.1706931146.c46e18eeb47a.4146.0 +2 -2
README.md CHANGED
@@ -1,6 +1,6 @@
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  ---
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- license: mit
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- base_model: laion/CLIP-ViT-H-14-laion2B-s32B-b79K
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  tags:
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  - generated_from_trainer
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  datasets:
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: F1
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  type: f1
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- value: 0.9434741496133529
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -30,10 +30,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # vit-SUPER02
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- This model is a fine-tuned version of [laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2246
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- - F1: 0.9435
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0002
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- - train_batch_size: 64
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  - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  | Training Loss | Epoch | Step | Validation Loss | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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- | 4.2088 | 0.3 | 50 | 4.0561 | 0.0670 |
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- | 2.9768 | 0.6 | 100 | 2.8147 | 0.2790 |
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- | 1.9733 | 0.9 | 150 | 1.7144 | 0.5560 |
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- | 1.1587 | 1.2 | 200 | 1.0544 | 0.7479 |
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- | 0.7738 | 1.5 | 250 | 0.6839 | 0.8392 |
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- | 0.5944 | 1.8 | 300 | 0.5771 | 0.8660 |
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- | 0.3746 | 2.1 | 350 | 0.5237 | 0.8636 |
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- | 0.4313 | 2.4 | 400 | 0.4649 | 0.8927 |
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- | 0.3874 | 2.69 | 450 | 0.3890 | 0.9015 |
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- | 0.346 | 2.99 | 500 | 0.3728 | 0.9072 |
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- | 0.3123 | 3.29 | 550 | 0.3296 | 0.9113 |
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- | 0.2976 | 3.59 | 600 | 0.3369 | 0.9166 |
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- | 0.2371 | 3.89 | 650 | 0.3207 | 0.9139 |
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- | 0.1462 | 4.19 | 700 | 0.2997 | 0.9195 |
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- | 0.178 | 4.49 | 750 | 0.2870 | 0.9317 |
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- | 0.1489 | 4.79 | 800 | 0.3048 | 0.9216 |
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- | 0.1135 | 5.09 | 850 | 0.2626 | 0.9364 |
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- | 0.0992 | 5.39 | 900 | 0.2920 | 0.9291 |
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- | 0.0879 | 5.69 | 950 | 0.2536 | 0.9365 |
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- | 0.0908 | 5.99 | 1000 | 0.2315 | 0.9435 |
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- | 0.071 | 6.29 | 1050 | 0.2542 | 0.9378 |
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- | 0.047 | 6.59 | 1100 | 0.2517 | 0.9426 |
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- | 0.0565 | 6.89 | 1150 | 0.2513 | 0.9365 |
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- | 0.0119 | 7.19 | 1200 | 0.2293 | 0.9431 |
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- | 0.0142 | 7.49 | 1250 | 0.2454 | 0.9414 |
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- | 0.0124 | 7.78 | 1300 | 0.2391 | 0.9432 |
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- | 0.0057 | 8.08 | 1350 | 0.2355 | 0.9446 |
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- | 0.0041 | 8.38 | 1400 | 0.2242 | 0.9520 |
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- | 0.0107 | 8.68 | 1450 | 0.2230 | 0.9466 |
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- | 0.009 | 8.98 | 1500 | 0.2236 | 0.9495 |
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- | 0.0027 | 9.28 | 1550 | 0.2274 | 0.9466 |
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- | 0.0027 | 9.58 | 1600 | 0.2241 | 0.9454 |
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- | 0.0022 | 9.88 | 1650 | 0.2246 | 0.9435 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
 
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  ---
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+ license: apache-2.0
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+ base_model: google/vit-large-patch16-224
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  tags:
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  - generated_from_trainer
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  datasets:
 
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  metrics:
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  - name: F1
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  type: f1
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+ value: 1.0
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  # vit-SUPER02
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+ This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0000
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+ - F1: 1.0
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 0.0002
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+ - train_batch_size: 32
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  - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 
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  | Training Loss | Epoch | Step | Validation Loss | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|
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+ | 0.0798 | 0.16 | 50 | 0.0393 | 0.9904 |
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+ | 0.0161 | 0.31 | 100 | 0.0176 | 0.9936 |
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+ | 0.0017 | 0.47 | 150 | 0.0020 | 0.9984 |
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+ | 0.0012 | 0.62 | 200 | 0.0026 | 0.9985 |
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+ | 0.0001 | 0.78 | 250 | 0.0001 | 1.0 |
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+ | 0.0001 | 0.93 | 300 | 0.0001 | 1.0 |
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+ | 0.0001 | 1.09 | 350 | 0.0001 | 1.0 |
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+ | 0.0 | 1.24 | 400 | 0.0000 | 1.0 |
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+ | 0.0 | 1.4 | 450 | 0.0000 | 1.0 |
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+ | 0.0 | 1.55 | 500 | 0.0000 | 1.0 |
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+ | 0.0 | 1.71 | 550 | 0.0000 | 1.0 |
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+ | 0.0 | 1.86 | 600 | 0.0000 | 1.0 |
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+ | 0.0 | 2.02 | 650 | 0.0000 | 1.0 |
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+ | 0.0 | 2.17 | 700 | 0.0000 | 1.0 |
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+ | 0.0 | 2.33 | 750 | 0.0000 | 1.0 |
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+ | 0.0 | 2.48 | 800 | 0.0000 | 1.0 |
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+ | 0.0 | 2.64 | 850 | 0.0000 | 1.0 |
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+ | 0.0 | 2.8 | 900 | 0.0000 | 1.0 |
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+ | 0.0 | 2.95 | 950 | 0.0000 | 1.0 |
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+ | 0.0 | 3.11 | 1000 | 0.0000 | 1.0 |
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+ | 0.0 | 3.26 | 1050 | 0.0000 | 1.0 |
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+ | 0.0 | 3.42 | 1100 | 0.0000 | 1.0 |
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+ | 0.0 | 3.57 | 1150 | 0.0000 | 1.0 |
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+ | 0.0 | 3.73 | 1200 | 0.0000 | 1.0 |
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+ | 0.0 | 3.88 | 1250 | 0.0000 | 1.0 |
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+ | 0.0 | 4.04 | 1300 | 0.0000 | 1.0 |
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+ | 0.0 | 4.19 | 1350 | 0.0000 | 1.0 |
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+ | 0.0 | 4.35 | 1400 | 0.0000 | 1.0 |
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+ | 0.0 | 4.5 | 1450 | 0.0000 | 1.0 |
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+ | 0.0 | 4.66 | 1500 | 0.0000 | 1.0 |
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+ | 0.0 | 4.81 | 1550 | 0.0000 | 1.0 |
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+ | 0.0 | 4.97 | 1600 | 0.0000 | 1.0 |
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+ | 0.0 | 5.12 | 1650 | 0.0000 | 1.0 |
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+ | 0.0 | 5.28 | 1700 | 0.0000 | 1.0 |
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+ | 0.0 | 5.43 | 1750 | 0.0000 | 1.0 |
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+ | 0.0 | 5.59 | 1800 | 0.0000 | 1.0 |
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+ | 0.0 | 5.75 | 1850 | 0.0000 | 1.0 |
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+ | 0.0 | 5.9 | 1900 | 0.0000 | 1.0 |
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+ | 0.0 | 6.06 | 1950 | 0.0000 | 1.0 |
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+ | 0.0 | 6.21 | 2000 | 0.0000 | 1.0 |
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+ | 0.0 | 6.37 | 2050 | 0.0000 | 1.0 |
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+ | 0.0 | 6.52 | 2100 | 0.0000 | 1.0 |
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+ | 0.0 | 6.68 | 2150 | 0.0000 | 1.0 |
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+ | 0.0 | 6.83 | 2200 | 0.0000 | 1.0 |
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+ | 0.0 | 6.99 | 2250 | 0.0000 | 1.0 |
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+ | 0.0 | 7.14 | 2300 | 0.0000 | 1.0 |
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+ | 0.0 | 7.3 | 2350 | 0.0000 | 1.0 |
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+ | 0.0 | 7.45 | 2400 | 0.0000 | 1.0 |
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+ | 0.0 | 7.61 | 2450 | 0.0000 | 1.0 |
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+ | 0.0 | 7.76 | 2500 | 0.0000 | 1.0 |
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+ | 0.0 | 7.92 | 2550 | 0.0000 | 1.0 |
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+ | 0.0 | 8.07 | 2600 | 0.0000 | 1.0 |
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+ | 0.0 | 8.23 | 2650 | 0.0000 | 1.0 |
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+ | 0.0 | 8.39 | 2700 | 0.0000 | 1.0 |
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+ | 0.0 | 8.54 | 2750 | 0.0000 | 1.0 |
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+ | 0.0 | 8.7 | 2800 | 0.0000 | 1.0 |
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+ | 0.0 | 8.85 | 2850 | 0.0000 | 1.0 |
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+ | 0.0 | 9.01 | 2900 | 0.0000 | 1.0 |
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+ | 0.0 | 9.16 | 2950 | 0.0000 | 1.0 |
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+ | 0.0 | 9.32 | 3000 | 0.0000 | 1.0 |
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+ | 0.0 | 9.47 | 3050 | 0.0000 | 1.0 |
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+ | 0.0 | 9.63 | 3100 | 0.0000 | 1.0 |
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+ | 0.0 | 9.78 | 3150 | 0.0000 | 1.0 |
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+ | 0.0 | 9.94 | 3200 | 0.0000 | 1.0 |
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  ### Framework versions
runs/Feb03_03-32-21_c46e18eeb47a/events.out.tfevents.1706931146.c46e18eeb47a.4146.0 CHANGED
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