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BilalMuftuoglu
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beit-base-patch16-224-55-fold3

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  1. README.md +163 -0
  2. model.safetensors +1 -1
  3. runs/May20_13-17-52_b94dbfcd7693/events.out.tfevents.1716211072.b94dbfcd7693.1289.2 +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-fold3
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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: 0.7848101265822784
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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-fold3
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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: 0.7686
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+ - Accuracy: 0.7848
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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 | 0.8571 | 3 | 0.8417 | 0.4810 |
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+ | No log | 2.0 | 7 | 0.6764 | 0.5823 |
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+ | 0.71 | 2.8571 | 10 | 0.7272 | 0.5316 |
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+ | 0.71 | 4.0 | 14 | 0.6122 | 0.6835 |
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+ | 0.71 | 4.8571 | 17 | 0.6288 | 0.5949 |
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+ | 0.6227 | 6.0 | 21 | 0.6550 | 0.5949 |
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+ | 0.6227 | 6.8571 | 24 | 0.6240 | 0.6329 |
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+ | 0.6227 | 8.0 | 28 | 0.5877 | 0.6709 |
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+ | 0.5472 | 8.8571 | 31 | 0.7285 | 0.5823 |
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+ | 0.5472 | 10.0 | 35 | 0.8305 | 0.5823 |
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+ | 0.5472 | 10.8571 | 38 | 0.5102 | 0.7848 |
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+ | 0.4766 | 12.0 | 42 | 0.5352 | 0.7215 |
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+ | 0.4766 | 12.8571 | 45 | 0.5357 | 0.6962 |
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+ | 0.4766 | 14.0 | 49 | 0.7418 | 0.6329 |
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+ | 0.408 | 14.8571 | 52 | 0.6150 | 0.6835 |
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+ | 0.408 | 16.0 | 56 | 0.4870 | 0.7975 |
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+ | 0.408 | 16.8571 | 59 | 0.6427 | 0.6962 |
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+ | 0.4078 | 18.0 | 63 | 0.4822 | 0.8101 |
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+ | 0.4078 | 18.8571 | 66 | 0.4947 | 0.7975 |
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+ | 0.3478 | 20.0 | 70 | 0.6847 | 0.7089 |
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+ | 0.3478 | 20.8571 | 73 | 0.6154 | 0.7342 |
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+ | 0.3478 | 22.0 | 77 | 0.5384 | 0.8101 |
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+ | 0.3006 | 22.8571 | 80 | 0.5939 | 0.7595 |
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+ | 0.3006 | 24.0 | 84 | 0.5214 | 0.7595 |
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+ | 0.3006 | 24.8571 | 87 | 0.5452 | 0.7342 |
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+ | 0.2977 | 26.0 | 91 | 0.6153 | 0.7215 |
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+ | 0.2977 | 26.8571 | 94 | 0.4730 | 0.7975 |
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+ | 0.2977 | 28.0 | 98 | 0.4861 | 0.7342 |
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+ | 0.2768 | 28.8571 | 101 | 0.6705 | 0.7342 |
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+ | 0.2768 | 30.0 | 105 | 0.6362 | 0.7848 |
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+ | 0.2768 | 30.8571 | 108 | 0.6548 | 0.7848 |
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+ | 0.2348 | 32.0 | 112 | 0.5100 | 0.7848 |
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+ | 0.2348 | 32.8571 | 115 | 0.7156 | 0.7595 |
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+ | 0.2348 | 34.0 | 119 | 0.4859 | 0.8228 |
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+ | 0.2199 | 34.8571 | 122 | 0.8490 | 0.7342 |
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+ | 0.2199 | 36.0 | 126 | 0.6095 | 0.7468 |
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+ | 0.2199 | 36.8571 | 129 | 0.6427 | 0.7468 |
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+ | 0.201 | 38.0 | 133 | 0.6283 | 0.7848 |
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+ | 0.201 | 38.8571 | 136 | 0.8883 | 0.7595 |
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+ | 0.1868 | 40.0 | 140 | 0.7146 | 0.7975 |
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+ | 0.1868 | 40.8571 | 143 | 1.3800 | 0.6962 |
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+ | 0.1868 | 42.0 | 147 | 0.5908 | 0.7848 |
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+ | 0.2011 | 42.8571 | 150 | 0.6158 | 0.7722 |
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+ | 0.2011 | 44.0 | 154 | 0.5477 | 0.7975 |
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+ | 0.2011 | 44.8571 | 157 | 0.8354 | 0.7722 |
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+ | 0.1807 | 46.0 | 161 | 0.7830 | 0.7848 |
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+ | 0.1807 | 46.8571 | 164 | 0.6327 | 0.8228 |
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+ | 0.1807 | 48.0 | 168 | 0.7858 | 0.7595 |
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+ | 0.1579 | 48.8571 | 171 | 0.8322 | 0.7342 |
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+ | 0.1579 | 50.0 | 175 | 0.7501 | 0.7848 |
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+ | 0.1579 | 50.8571 | 178 | 0.8303 | 0.8228 |
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+ | 0.2066 | 52.0 | 182 | 0.6831 | 0.7595 |
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+ | 0.2066 | 52.8571 | 185 | 0.7837 | 0.8228 |
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+ | 0.2066 | 54.0 | 189 | 0.5597 | 0.8354 |
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+ | 0.1647 | 54.8571 | 192 | 0.5484 | 0.8354 |
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+ | 0.1647 | 56.0 | 196 | 1.0047 | 0.7848 |
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+ | 0.1647 | 56.8571 | 199 | 0.7815 | 0.8228 |
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+ | 0.1404 | 58.0 | 203 | 0.6808 | 0.7975 |
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+ | 0.1404 | 58.8571 | 206 | 1.0068 | 0.8101 |
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+ | 0.1451 | 60.0 | 210 | 0.7698 | 0.8228 |
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+ | 0.1451 | 60.8571 | 213 | 0.6495 | 0.8228 |
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+ | 0.1451 | 62.0 | 217 | 0.7066 | 0.8354 |
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+ | 0.1341 | 62.8571 | 220 | 0.6250 | 0.8354 |
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+ | 0.1341 | 64.0 | 224 | 0.5573 | 0.7975 |
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+ | 0.1341 | 64.8571 | 227 | 0.6051 | 0.8101 |
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+ | 0.127 | 66.0 | 231 | 0.7576 | 0.8101 |
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+ | 0.127 | 66.8571 | 234 | 0.8297 | 0.8101 |
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+ | 0.127 | 68.0 | 238 | 1.0732 | 0.7975 |
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+ | 0.1129 | 68.8571 | 241 | 1.0503 | 0.7975 |
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+ | 0.1129 | 70.0 | 245 | 0.7520 | 0.8101 |
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+ | 0.1129 | 70.8571 | 248 | 0.6825 | 0.8354 |
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+ | 0.1205 | 72.0 | 252 | 0.7002 | 0.7975 |
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+ | 0.1205 | 72.8571 | 255 | 0.7430 | 0.8101 |
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+ | 0.1205 | 74.0 | 259 | 0.7610 | 0.7975 |
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+ | 0.1199 | 74.8571 | 262 | 0.6854 | 0.8101 |
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+ | 0.1199 | 76.0 | 266 | 0.6767 | 0.8354 |
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+ | 0.1199 | 76.8571 | 269 | 0.6685 | 0.8354 |
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+ | 0.1165 | 78.0 | 273 | 0.7134 | 0.7848 |
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+ | 0.1165 | 78.8571 | 276 | 0.7344 | 0.7848 |
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+ | 0.1213 | 80.0 | 280 | 0.7403 | 0.7722 |
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+ | 0.1213 | 80.8571 | 283 | 0.7818 | 0.7848 |
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+ | 0.1213 | 82.0 | 287 | 0.7620 | 0.7722 |
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+ | 0.1024 | 82.8571 | 290 | 0.7539 | 0.7722 |
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+ | 0.1024 | 84.0 | 294 | 0.7659 | 0.7722 |
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+ | 0.1024 | 84.8571 | 297 | 0.7686 | 0.7848 |
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+ | 0.1109 | 85.7143 | 300 | 0.7686 | 0.7848 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.40.2
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+ - Pytorch 2.2.1+cu121
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+ - Datasets 2.19.1
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+ - Tokenizers 0.19.1
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