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vit-base-patch16-224-dmae-va-U5-100-iN

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  2. model.safetensors +1 -1
  3. runs/Apr26_08-18-02_DESKTOP-SKBE9FB/events.out.tfevents.1714141084.DESKTOP-SKBE9FB.7308.0 +2 -2
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: google/vit-base-patch16-224
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: vit-base-patch16-224-dmae-va-U5-100-iN
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+ results: []
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+ ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # vit-base-patch16-224-dmae-va-U5-100-iN
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+
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+ This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.9858
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+ - Accuracy: 0.7833
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 32
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 128
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - lr_scheduler_warmup_ratio: 0.05
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+ - num_epochs: 100
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 0.9 | 7 | 1.3812 | 0.45 |
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+ | 1.3848 | 1.94 | 15 | 1.3606 | 0.5 |
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+ | 1.3686 | 2.97 | 23 | 1.3075 | 0.5333 |
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+ | 1.2965 | 4.0 | 31 | 1.2370 | 0.4667 |
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+ | 1.2965 | 4.9 | 38 | 1.1168 | 0.5333 |
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+ | 1.1753 | 5.94 | 46 | 1.0310 | 0.5667 |
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+ | 1.0294 | 6.97 | 54 | 0.9316 | 0.6 |
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+ | 0.902 | 8.0 | 62 | 0.8728 | 0.6833 |
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+ | 0.902 | 8.9 | 69 | 0.8129 | 0.7667 |
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+ | 0.7812 | 9.94 | 77 | 0.7006 | 0.8 |
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+ | 0.6419 | 10.97 | 85 | 0.6381 | 0.8667 |
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+ | 0.5109 | 12.0 | 93 | 0.6327 | 0.8167 |
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+ | 0.3838 | 12.9 | 100 | 0.5442 | 0.8667 |
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+ | 0.3838 | 13.94 | 108 | 0.6755 | 0.75 |
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+ | 0.285 | 14.97 | 116 | 0.7756 | 0.7167 |
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+ | 0.2672 | 16.0 | 124 | 0.8107 | 0.7167 |
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+ | 0.2466 | 16.9 | 131 | 0.5219 | 0.8333 |
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+ | 0.2466 | 17.94 | 139 | 0.7041 | 0.7833 |
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+ | 0.2312 | 18.97 | 147 | 0.7879 | 0.75 |
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+ | 0.1933 | 20.0 | 155 | 0.7090 | 0.8 |
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+ | 0.1692 | 20.9 | 162 | 0.5395 | 0.8333 |
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+ | 0.1578 | 21.94 | 170 | 0.6419 | 0.8167 |
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+ | 0.1578 | 22.97 | 178 | 0.5736 | 0.8333 |
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+ | 0.1321 | 24.0 | 186 | 0.7471 | 0.75 |
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+ | 0.1114 | 24.9 | 193 | 0.6447 | 0.7667 |
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+ | 0.1385 | 25.94 | 201 | 0.6158 | 0.8167 |
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+ | 0.1385 | 26.97 | 209 | 0.6467 | 0.8 |
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+ | 0.1136 | 28.0 | 217 | 0.6180 | 0.85 |
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+ | 0.0997 | 28.9 | 224 | 0.8578 | 0.75 |
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+ | 0.1064 | 29.94 | 232 | 0.6778 | 0.8167 |
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+ | 0.0775 | 30.97 | 240 | 0.8124 | 0.8 |
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+ | 0.0775 | 32.0 | 248 | 0.7783 | 0.8 |
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+ | 0.0921 | 32.9 | 255 | 0.8320 | 0.7333 |
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+ | 0.0919 | 33.94 | 263 | 0.8310 | 0.7833 |
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+ | 0.0888 | 34.97 | 271 | 0.6576 | 0.85 |
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+ | 0.0888 | 36.0 | 279 | 0.7044 | 0.8333 |
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+ | 0.0693 | 36.9 | 286 | 0.7608 | 0.8167 |
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+ | 0.061 | 37.94 | 294 | 0.7802 | 0.8 |
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+ | 0.0699 | 38.97 | 302 | 0.7762 | 0.8167 |
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+ | 0.0652 | 40.0 | 310 | 0.7579 | 0.8 |
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+ | 0.0652 | 40.9 | 317 | 0.9985 | 0.75 |
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+ | 0.0562 | 41.94 | 325 | 0.8027 | 0.8167 |
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+ | 0.0534 | 42.97 | 333 | 0.9705 | 0.7833 |
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+ | 0.0519 | 44.0 | 341 | 0.7301 | 0.8333 |
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+ | 0.0519 | 44.9 | 348 | 0.8433 | 0.8 |
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+ | 0.0529 | 45.94 | 356 | 0.8534 | 0.8 |
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+ | 0.0772 | 46.97 | 364 | 0.8562 | 0.8 |
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+ | 0.0644 | 48.0 | 372 | 0.8419 | 0.8 |
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+ | 0.0644 | 48.9 | 379 | 1.1251 | 0.7667 |
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+ | 0.0467 | 49.94 | 387 | 0.7537 | 0.8333 |
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+ | 0.0576 | 50.97 | 395 | 0.7517 | 0.8333 |
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+ | 0.0344 | 52.0 | 403 | 0.8343 | 0.8 |
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+ | 0.0663 | 52.9 | 410 | 0.7636 | 0.8 |
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+ | 0.0663 | 53.94 | 418 | 0.8253 | 0.8167 |
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+ | 0.0353 | 54.97 | 426 | 0.9348 | 0.8 |
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+ | 0.0524 | 56.0 | 434 | 0.8217 | 0.8167 |
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+ | 0.0479 | 56.9 | 441 | 0.7586 | 0.8167 |
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+ | 0.0479 | 57.94 | 449 | 0.8147 | 0.8 |
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+ | 0.0595 | 58.97 | 457 | 1.0000 | 0.7833 |
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+ | 0.0475 | 60.0 | 465 | 0.9291 | 0.7833 |
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+ | 0.049 | 60.9 | 472 | 0.9588 | 0.7833 |
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+ | 0.0398 | 61.94 | 480 | 0.9501 | 0.8 |
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+ | 0.0398 | 62.97 | 488 | 0.9499 | 0.8 |
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+ | 0.0496 | 64.0 | 496 | 0.9279 | 0.8 |
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+ | 0.0354 | 64.9 | 503 | 0.9677 | 0.75 |
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+ | 0.0325 | 65.94 | 511 | 0.8371 | 0.8333 |
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+ | 0.0325 | 66.97 | 519 | 0.9683 | 0.8 |
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+ | 0.0335 | 68.0 | 527 | 1.0455 | 0.7833 |
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+ | 0.0375 | 68.9 | 534 | 0.9027 | 0.8167 |
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+ | 0.0424 | 69.94 | 542 | 0.8043 | 0.85 |
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+ | 0.0383 | 70.97 | 550 | 0.9035 | 0.7833 |
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+ | 0.0383 | 72.0 | 558 | 0.9360 | 0.7833 |
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+ | 0.0295 | 72.9 | 565 | 0.9841 | 0.7833 |
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+ | 0.0307 | 73.94 | 573 | 0.9300 | 0.8 |
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+ | 0.0376 | 74.97 | 581 | 0.9630 | 0.7833 |
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+ | 0.0376 | 76.0 | 589 | 0.9777 | 0.7833 |
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+ | 0.0259 | 76.9 | 596 | 0.9323 | 0.8 |
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+ | 0.0345 | 77.94 | 604 | 0.9075 | 0.8 |
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+ | 0.0346 | 78.97 | 612 | 0.8951 | 0.8 |
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+ | 0.0319 | 80.0 | 620 | 0.9676 | 0.8 |
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+ | 0.0319 | 80.9 | 627 | 0.9884 | 0.8 |
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+ | 0.0226 | 81.94 | 635 | 0.9851 | 0.7833 |
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+ | 0.033 | 82.97 | 643 | 0.9710 | 0.7833 |
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+ | 0.0262 | 84.0 | 651 | 0.9851 | 0.7833 |
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+ | 0.0262 | 84.9 | 658 | 0.9868 | 0.7833 |
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+ | 0.0345 | 85.94 | 666 | 0.9702 | 0.7833 |
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+ | 0.0299 | 86.97 | 674 | 0.9889 | 0.7833 |
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+ | 0.0347 | 88.0 | 682 | 1.0003 | 0.7833 |
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+ | 0.0347 | 88.9 | 689 | 0.9913 | 0.7833 |
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+ | 0.0288 | 89.94 | 697 | 0.9859 | 0.7833 |
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+ | 0.0198 | 90.32 | 700 | 0.9858 | 0.7833 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.36.2
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+ - Pytorch 2.1.2+cu118
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.0
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