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BilalMuftuoglu
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beit-base-patch16-224-hasta-75-fold1

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  2. model.safetensors +1 -1
  3. runs/May23_14-31-34_2bc4f829fe52/events.out.tfevents.1716474695.2bc4f829fe52.989.20 +2 -2
README.md ADDED
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+ ---
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+ license: apache-2.0
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+ base_model: microsoft/beit-base-patch16-224
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+ tags:
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+ - generated_from_trainer
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+ datasets:
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+ - imagefolder
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: beit-base-patch16-224-hasta-75-fold1
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+ results:
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+ - task:
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+ name: Image Classification
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+ type: image-classification
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+ dataset:
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+ name: imagefolder
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+ type: imagefolder
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+ config: default
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+ split: train
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+ args: default
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+ metrics:
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+ - name: Accuracy
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+ type: accuracy
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+ value: 1.0
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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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+ # beit-base-patch16-224-hasta-75-fold1
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+
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+ This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0442
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+ - Accuracy: 1.0
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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: 0.1
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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 | 1.0 | 1 | 2.2077 | 0.0 |
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+ | No log | 2.0 | 2 | 1.7828 | 0.0 |
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+ | No log | 3.0 | 3 | 1.0543 | 0.3333 |
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+ | No log | 4.0 | 4 | 0.4305 | 0.9167 |
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+ | No log | 5.0 | 5 | 0.2672 | 0.9167 |
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+ | No log | 6.0 | 6 | 0.3206 | 0.9167 |
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+ | No log | 7.0 | 7 | 0.3318 | 0.9167 |
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+ | No log | 8.0 | 8 | 0.3007 | 0.9167 |
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+ | No log | 9.0 | 9 | 0.3268 | 0.9167 |
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+ | 0.4863 | 10.0 | 10 | 0.3425 | 0.9167 |
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+ | 0.4863 | 11.0 | 11 | 0.2906 | 0.9167 |
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+ | 0.4863 | 12.0 | 12 | 0.2639 | 0.9167 |
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+ | 0.4863 | 13.0 | 13 | 0.2962 | 0.9167 |
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+ | 0.4863 | 14.0 | 14 | 0.4442 | 0.8333 |
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+ | 0.4863 | 15.0 | 15 | 0.3108 | 0.8333 |
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+ | 0.4863 | 16.0 | 16 | 0.2321 | 0.9167 |
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+ | 0.4863 | 17.0 | 17 | 0.2309 | 0.9167 |
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+ | 0.4863 | 18.0 | 18 | 0.2432 | 1.0 |
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+ | 0.4863 | 19.0 | 19 | 0.2240 | 1.0 |
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+ | 0.1603 | 20.0 | 20 | 0.1608 | 0.9167 |
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+ | 0.1603 | 21.0 | 21 | 0.1275 | 0.9167 |
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+ | 0.1603 | 22.0 | 22 | 0.1191 | 0.9167 |
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+ | 0.1603 | 23.0 | 23 | 0.1030 | 0.9167 |
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+ | 0.1603 | 24.0 | 24 | 0.1010 | 1.0 |
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+ | 0.1603 | 25.0 | 25 | 0.0816 | 1.0 |
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+ | 0.1603 | 26.0 | 26 | 0.1814 | 0.9167 |
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+ | 0.1603 | 27.0 | 27 | 0.1654 | 0.9167 |
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+ | 0.1603 | 28.0 | 28 | 0.0945 | 1.0 |
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+ | 0.1603 | 29.0 | 29 | 0.0847 | 1.0 |
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+ | 0.1007 | 30.0 | 30 | 0.1566 | 1.0 |
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+ | 0.1007 | 31.0 | 31 | 0.0819 | 1.0 |
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+ | 0.1007 | 32.0 | 32 | 0.0782 | 1.0 |
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+ | 0.1007 | 33.0 | 33 | 0.0781 | 1.0 |
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+ | 0.1007 | 34.0 | 34 | 0.0635 | 1.0 |
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+ | 0.1007 | 35.0 | 35 | 0.0675 | 1.0 |
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+ | 0.1007 | 36.0 | 36 | 0.1137 | 1.0 |
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+ | 0.1007 | 37.0 | 37 | 0.1267 | 0.9167 |
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+ | 0.1007 | 38.0 | 38 | 0.1438 | 0.9167 |
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+ | 0.1007 | 39.0 | 39 | 0.1301 | 0.9167 |
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+ | 0.0573 | 40.0 | 40 | 0.1123 | 0.9167 |
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+ | 0.0573 | 41.0 | 41 | 0.0673 | 1.0 |
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+ | 0.0573 | 42.0 | 42 | 0.0265 | 1.0 |
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+ | 0.0573 | 43.0 | 43 | 0.0317 | 1.0 |
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+ | 0.0573 | 44.0 | 44 | 0.0461 | 1.0 |
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+ | 0.0573 | 45.0 | 45 | 0.0326 | 1.0 |
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+ | 0.0573 | 46.0 | 46 | 0.0221 | 1.0 |
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+ | 0.0573 | 47.0 | 47 | 0.0227 | 1.0 |
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+ | 0.0573 | 48.0 | 48 | 0.0214 | 1.0 |
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+ | 0.0573 | 49.0 | 49 | 0.0176 | 1.0 |
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+ | 0.0566 | 50.0 | 50 | 0.0150 | 1.0 |
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+ | 0.0566 | 51.0 | 51 | 0.0154 | 1.0 |
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+ | 0.0566 | 52.0 | 52 | 0.0139 | 1.0 |
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+ | 0.0566 | 53.0 | 53 | 0.0097 | 1.0 |
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+ | 0.0566 | 54.0 | 54 | 0.0143 | 1.0 |
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+ | 0.0566 | 55.0 | 55 | 0.0272 | 1.0 |
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+ | 0.0566 | 56.0 | 56 | 0.0427 | 1.0 |
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+ | 0.0566 | 57.0 | 57 | 0.0343 | 1.0 |
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+ | 0.0566 | 58.0 | 58 | 0.0290 | 1.0 |
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+ | 0.0566 | 59.0 | 59 | 0.0557 | 1.0 |
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+ | 0.0242 | 60.0 | 60 | 0.0905 | 1.0 |
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+ | 0.0242 | 61.0 | 61 | 0.1374 | 0.9167 |
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+ | 0.0242 | 62.0 | 62 | 0.1763 | 0.9167 |
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+ | 0.0242 | 63.0 | 63 | 0.1793 | 0.9167 |
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+ | 0.0242 | 64.0 | 64 | 0.1640 | 0.9167 |
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+ | 0.0242 | 65.0 | 65 | 0.1445 | 0.9167 |
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+ | 0.0242 | 66.0 | 66 | 0.1092 | 1.0 |
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+ | 0.0242 | 67.0 | 67 | 0.0915 | 1.0 |
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+ | 0.0242 | 68.0 | 68 | 0.0640 | 1.0 |
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+ | 0.0242 | 69.0 | 69 | 0.0376 | 1.0 |
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+ | 0.0339 | 70.0 | 70 | 0.0297 | 1.0 |
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+ | 0.0339 | 71.0 | 71 | 0.0238 | 1.0 |
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+ | 0.0339 | 72.0 | 72 | 0.0178 | 1.0 |
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+ | 0.0339 | 73.0 | 73 | 0.0104 | 1.0 |
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+ | 0.0339 | 74.0 | 74 | 0.0063 | 1.0 |
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+ | 0.0339 | 75.0 | 75 | 0.0042 | 1.0 |
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+ | 0.0339 | 76.0 | 76 | 0.0031 | 1.0 |
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+ | 0.0339 | 77.0 | 77 | 0.0029 | 1.0 |
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+ | 0.0339 | 78.0 | 78 | 0.0034 | 1.0 |
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+ | 0.0339 | 79.0 | 79 | 0.0035 | 1.0 |
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+ | 0.0532 | 80.0 | 80 | 0.0035 | 1.0 |
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+ | 0.0532 | 81.0 | 81 | 0.0039 | 1.0 |
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+ | 0.0532 | 82.0 | 82 | 0.0054 | 1.0 |
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+ | 0.0532 | 83.0 | 83 | 0.0110 | 1.0 |
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+ | 0.0532 | 84.0 | 84 | 0.0255 | 1.0 |
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+ | 0.0532 | 85.0 | 85 | 0.0500 | 1.0 |
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+ | 0.0532 | 86.0 | 86 | 0.0844 | 0.9167 |
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+ | 0.0532 | 87.0 | 87 | 0.1191 | 0.9167 |
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+ | 0.0532 | 88.0 | 88 | 0.1437 | 0.9167 |
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+ | 0.0532 | 89.0 | 89 | 0.1564 | 0.9167 |
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+ | 0.0316 | 90.0 | 90 | 0.1544 | 0.9167 |
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+ | 0.0316 | 91.0 | 91 | 0.1455 | 0.9167 |
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+ | 0.0316 | 92.0 | 92 | 0.1383 | 0.9167 |
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+ | 0.0316 | 93.0 | 93 | 0.1194 | 0.9167 |
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+ | 0.0316 | 94.0 | 94 | 0.1027 | 0.9167 |
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+ | 0.0316 | 95.0 | 95 | 0.0875 | 0.9167 |
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+ | 0.0316 | 96.0 | 96 | 0.0715 | 1.0 |
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+ | 0.0316 | 97.0 | 97 | 0.0608 | 1.0 |
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+ | 0.0316 | 98.0 | 98 | 0.0519 | 1.0 |
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+ | 0.0316 | 99.0 | 99 | 0.0468 | 1.0 |
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+ | 0.0299 | 100.0 | 100 | 0.0442 | 1.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.41.0
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+ - Pytorch 2.3.0+cu121
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+ - Datasets 2.19.1
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+ - Tokenizers 0.19.1
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