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nicolasdupuisroy
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vit-gabor-detection-v2

Image Classification
Transformers
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vit
vision
Generated from Trainer
Eval Results
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  2. model.safetensors +1 -1
  3. runs/Dec19_00-25-43_b6c43624a1ee/events.out.tfevents.1702945548.b6c43624a1ee.6546.0 +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-in21k
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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: vit-gabor-detection-v2
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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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+ # vit-gabor-detection-v2
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+
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+ This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0185
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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: 2e-05
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+ - train_batch_size: 200
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+ - eval_batch_size: 200
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+ - seed: 1337
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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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+ - num_epochs: 120.0
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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 | 0.5751 | 1.0 |
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+ | No log | 2.0 | 2 | 0.5081 | 1.0 |
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+ | No log | 3.0 | 3 | 0.4654 | 1.0 |
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+ | No log | 4.0 | 4 | 0.4014 | 1.0 |
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+ | No log | 5.0 | 5 | 0.3692 | 1.0 |
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+ | No log | 6.0 | 6 | 0.3327 | 1.0 |
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+ | No log | 7.0 | 7 | 0.2937 | 1.0 |
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+ | No log | 8.0 | 8 | 0.2775 | 1.0 |
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+ | No log | 9.0 | 9 | 0.2335 | 1.0 |
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+ | 0.4432 | 10.0 | 10 | 0.2092 | 1.0 |
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+ | 0.4432 | 11.0 | 11 | 0.2007 | 1.0 |
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+ | 0.4432 | 12.0 | 12 | 0.1674 | 1.0 |
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+ | 0.4432 | 13.0 | 13 | 0.1546 | 1.0 |
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+ | 0.4432 | 14.0 | 14 | 0.1393 | 1.0 |
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+ | 0.4432 | 15.0 | 15 | 0.1297 | 1.0 |
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+ | 0.4432 | 16.0 | 16 | 0.1219 | 1.0 |
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+ | 0.4432 | 17.0 | 17 | 0.1090 | 1.0 |
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+ | 0.4432 | 18.0 | 18 | 0.1012 | 1.0 |
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+ | 0.4432 | 19.0 | 19 | 0.0981 | 1.0 |
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+ | 0.1696 | 20.0 | 20 | 0.0874 | 1.0 |
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+ | 0.1696 | 21.0 | 21 | 0.0812 | 1.0 |
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+ | 0.1696 | 22.0 | 22 | 0.0750 | 1.0 |
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+ | 0.1696 | 23.0 | 23 | 0.0754 | 1.0 |
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+ | 0.1696 | 24.0 | 24 | 0.0693 | 1.0 |
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+ | 0.1696 | 25.0 | 25 | 0.0642 | 1.0 |
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+ | 0.1696 | 26.0 | 26 | 0.0610 | 1.0 |
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+ | 0.1696 | 27.0 | 27 | 0.0586 | 1.0 |
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+ | 0.1696 | 28.0 | 28 | 0.0569 | 1.0 |
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+ | 0.1696 | 29.0 | 29 | 0.0532 | 1.0 |
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+ | 0.0792 | 30.0 | 30 | 0.0506 | 1.0 |
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+ | 0.0792 | 31.0 | 31 | 0.0495 | 1.0 |
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+ | 0.0792 | 32.0 | 32 | 0.0476 | 1.0 |
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+ | 0.0792 | 33.0 | 33 | 0.0457 | 1.0 |
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+ | 0.0792 | 34.0 | 34 | 0.0442 | 1.0 |
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+ | 0.0792 | 35.0 | 35 | 0.0419 | 1.0 |
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+ | 0.0792 | 36.0 | 36 | 0.0404 | 1.0 |
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+ | 0.0792 | 37.0 | 37 | 0.0396 | 1.0 |
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+ | 0.0792 | 38.0 | 38 | 0.0384 | 1.0 |
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+ | 0.0792 | 39.0 | 39 | 0.0377 | 1.0 |
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+ | 0.049 | 40.0 | 40 | 0.0366 | 1.0 |
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+ | 0.049 | 41.0 | 41 | 0.0370 | 1.0 |
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+ | 0.049 | 42.0 | 42 | 0.0339 | 1.0 |
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+ | 0.049 | 43.0 | 43 | 0.0330 | 1.0 |
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+ | 0.049 | 44.0 | 44 | 0.0344 | 1.0 |
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+ | 0.049 | 45.0 | 45 | 0.0324 | 1.0 |
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+ | 0.049 | 46.0 | 46 | 0.0323 | 1.0 |
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+ | 0.049 | 47.0 | 47 | 0.0311 | 1.0 |
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+ | 0.049 | 48.0 | 48 | 0.0308 | 1.0 |
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+ | 0.049 | 49.0 | 49 | 0.0294 | 1.0 |
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+ | 0.0359 | 50.0 | 50 | 0.0297 | 1.0 |
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+ | 0.0359 | 51.0 | 51 | 0.0289 | 1.0 |
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+ | 0.0359 | 52.0 | 52 | 0.0285 | 1.0 |
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+ | 0.0359 | 53.0 | 53 | 0.0280 | 1.0 |
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+ | 0.0359 | 54.0 | 54 | 0.0270 | 1.0 |
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+ | 0.0359 | 55.0 | 55 | 0.0265 | 1.0 |
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+ | 0.0359 | 56.0 | 56 | 0.0266 | 1.0 |
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+ | 0.0359 | 57.0 | 57 | 0.0261 | 1.0 |
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+ | 0.0359 | 58.0 | 58 | 0.0268 | 1.0 |
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+ | 0.0359 | 59.0 | 59 | 0.0255 | 1.0 |
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+ | 0.0293 | 60.0 | 60 | 0.0255 | 1.0 |
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+ | 0.0293 | 61.0 | 61 | 0.0246 | 1.0 |
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+ | 0.0293 | 62.0 | 62 | 0.0256 | 1.0 |
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+ | 0.0293 | 63.0 | 63 | 0.0247 | 1.0 |
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+ | 0.0293 | 64.0 | 64 | 0.0241 | 1.0 |
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+ | 0.0293 | 65.0 | 65 | 0.0241 | 1.0 |
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+ | 0.0293 | 66.0 | 66 | 0.0234 | 1.0 |
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+ | 0.0293 | 67.0 | 67 | 0.0236 | 1.0 |
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+ | 0.0293 | 68.0 | 68 | 0.0228 | 1.0 |
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+ | 0.0293 | 69.0 | 69 | 0.0233 | 1.0 |
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+ | 0.0256 | 70.0 | 70 | 0.0227 | 1.0 |
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+ | 0.0256 | 71.0 | 71 | 0.0227 | 1.0 |
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+ | 0.0256 | 72.0 | 72 | 0.0230 | 1.0 |
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+ | 0.0256 | 73.0 | 73 | 0.0222 | 1.0 |
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+ | 0.0256 | 74.0 | 74 | 0.0220 | 1.0 |
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+ | 0.0256 | 75.0 | 75 | 0.0221 | 1.0 |
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+ | 0.0256 | 76.0 | 76 | 0.0219 | 1.0 |
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+ | 0.0256 | 77.0 | 77 | 0.0215 | 1.0 |
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+ | 0.0256 | 78.0 | 78 | 0.0210 | 1.0 |
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+ | 0.0256 | 79.0 | 79 | 0.0209 | 1.0 |
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+ | 0.0234 | 80.0 | 80 | 0.0212 | 1.0 |
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+ | 0.0234 | 81.0 | 81 | 0.0212 | 1.0 |
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+ | 0.0234 | 82.0 | 82 | 0.0206 | 1.0 |
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+ | 0.0234 | 83.0 | 83 | 0.0210 | 1.0 |
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+ | 0.0234 | 84.0 | 84 | 0.0204 | 1.0 |
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+ | 0.0234 | 85.0 | 85 | 0.0205 | 1.0 |
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+ | 0.0234 | 86.0 | 86 | 0.0204 | 1.0 |
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+ | 0.0234 | 87.0 | 87 | 0.0203 | 1.0 |
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+ | 0.0234 | 88.0 | 88 | 0.0200 | 1.0 |
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+ | 0.0234 | 89.0 | 89 | 0.0203 | 1.0 |
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+ | 0.0218 | 90.0 | 90 | 0.0196 | 1.0 |
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+ | 0.0218 | 91.0 | 91 | 0.0199 | 1.0 |
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+ | 0.0218 | 92.0 | 92 | 0.0198 | 1.0 |
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+ | 0.0218 | 93.0 | 93 | 0.0196 | 1.0 |
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+ | 0.0218 | 94.0 | 94 | 0.0195 | 1.0 |
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+ | 0.0218 | 95.0 | 95 | 0.0198 | 1.0 |
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+ | 0.0218 | 96.0 | 96 | 0.0197 | 1.0 |
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+ | 0.0218 | 97.0 | 97 | 0.0193 | 1.0 |
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+ | 0.0218 | 98.0 | 98 | 0.0195 | 1.0 |
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+ | 0.0218 | 99.0 | 99 | 0.0194 | 1.0 |
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+ | 0.0208 | 100.0 | 100 | 0.0192 | 1.0 |
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+ | 0.0208 | 101.0 | 101 | 0.0190 | 1.0 |
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+ | 0.0208 | 102.0 | 102 | 0.0188 | 1.0 |
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+ | 0.0208 | 103.0 | 103 | 0.0191 | 1.0 |
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+ | 0.0208 | 104.0 | 104 | 0.0193 | 1.0 |
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+ | 0.0208 | 105.0 | 105 | 0.0193 | 1.0 |
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+ | 0.0208 | 106.0 | 106 | 0.0190 | 1.0 |
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+ | 0.0208 | 107.0 | 107 | 0.0191 | 1.0 |
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+ | 0.0208 | 108.0 | 108 | 0.0186 | 1.0 |
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+ | 0.0208 | 109.0 | 109 | 0.0188 | 1.0 |
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+ | 0.0202 | 110.0 | 110 | 0.0187 | 1.0 |
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+ | 0.0202 | 111.0 | 111 | 0.0191 | 1.0 |
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+ | 0.0202 | 112.0 | 112 | 0.0188 | 1.0 |
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+ | 0.0202 | 113.0 | 113 | 0.0185 | 1.0 |
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+ | 0.0202 | 114.0 | 114 | 0.0188 | 1.0 |
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+ | 0.0202 | 115.0 | 115 | 0.0183 | 1.0 |
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+ | 0.0202 | 116.0 | 116 | 0.0187 | 1.0 |
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+ | 0.0202 | 117.0 | 117 | 0.0185 | 1.0 |
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+ | 0.0202 | 118.0 | 118 | 0.0184 | 1.0 |
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+ | 0.0202 | 119.0 | 119 | 0.0188 | 1.0 |
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+ | 0.0197 | 120.0 | 120 | 0.0185 | 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 4.37.0.dev0
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+ - Pytorch 2.1.0+cu121
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+ - Datasets 2.15.0
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+ - Tokenizers 0.15.0
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