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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-85-fold5 |
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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: 0.7272727272727273 |
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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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# beit-base-patch16-224-hasta-85-fold5 |
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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: 2.2350 |
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- Accuracy: 0.7273 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| No log | 1.0 | 1 | 1.0103 | 0.5455 | |
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| No log | 2.0 | 2 | 0.8046 | 0.6364 | |
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| No log | 3.0 | 3 | 0.7378 | 0.7273 | |
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| No log | 4.0 | 4 | 1.0676 | 0.7273 | |
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| No log | 5.0 | 5 | 1.3956 | 0.7273 | |
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| No log | 6.0 | 6 | 1.5799 | 0.7273 | |
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| No log | 7.0 | 7 | 1.6061 | 0.7273 | |
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| No log | 8.0 | 8 | 1.4351 | 0.7273 | |
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| No log | 9.0 | 9 | 1.3296 | 0.7273 | |
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| 0.39 | 10.0 | 10 | 1.2816 | 0.7273 | |
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| 0.39 | 11.0 | 11 | 1.2655 | 0.7273 | |
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| 0.39 | 12.0 | 12 | 1.2040 | 0.7273 | |
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| 0.39 | 13.0 | 13 | 1.0664 | 0.7273 | |
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| 0.39 | 14.0 | 14 | 1.0846 | 0.7273 | |
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| 0.39 | 15.0 | 15 | 1.2145 | 0.7273 | |
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| 0.39 | 16.0 | 16 | 1.4682 | 0.7273 | |
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| 0.39 | 17.0 | 17 | 1.4473 | 0.7273 | |
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| 0.39 | 18.0 | 18 | 1.2699 | 0.7273 | |
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| 0.39 | 19.0 | 19 | 1.2467 | 0.7273 | |
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| 0.1832 | 20.0 | 20 | 1.2648 | 0.7273 | |
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| 0.1832 | 21.0 | 21 | 1.2914 | 0.7273 | |
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| 0.1832 | 22.0 | 22 | 1.3444 | 0.7273 | |
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| 0.1832 | 23.0 | 23 | 1.5325 | 0.7273 | |
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| 0.1832 | 24.0 | 24 | 1.6140 | 0.7273 | |
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| 0.1832 | 25.0 | 25 | 1.6262 | 0.7273 | |
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| 0.1832 | 26.0 | 26 | 1.6753 | 0.7273 | |
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| 0.1832 | 27.0 | 27 | 1.6531 | 0.7273 | |
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| 0.1832 | 28.0 | 28 | 1.7096 | 0.7273 | |
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| 0.1832 | 29.0 | 29 | 1.6662 | 0.7273 | |
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| 0.1194 | 30.0 | 30 | 1.5769 | 0.7273 | |
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| 0.1194 | 31.0 | 31 | 1.4447 | 0.7273 | |
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| 0.1194 | 32.0 | 32 | 1.2644 | 0.7273 | |
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| 0.1194 | 33.0 | 33 | 1.2033 | 0.7273 | |
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| 0.1194 | 34.0 | 34 | 1.2703 | 0.7273 | |
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| 0.1194 | 35.0 | 35 | 1.4492 | 0.7273 | |
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| 0.1194 | 36.0 | 36 | 1.5890 | 0.7273 | |
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| 0.1194 | 37.0 | 37 | 1.5691 | 0.7273 | |
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| 0.1194 | 38.0 | 38 | 1.4127 | 0.7273 | |
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| 0.1194 | 39.0 | 39 | 1.3179 | 0.7273 | |
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| 0.0783 | 40.0 | 40 | 1.2986 | 0.7273 | |
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| 0.0783 | 41.0 | 41 | 1.3181 | 0.7273 | |
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| 0.0783 | 42.0 | 42 | 1.4253 | 0.7273 | |
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| 0.0783 | 43.0 | 43 | 1.5179 | 0.7273 | |
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| 0.0783 | 44.0 | 44 | 1.5685 | 0.7273 | |
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| 0.0783 | 45.0 | 45 | 1.5696 | 0.7273 | |
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| 0.0783 | 46.0 | 46 | 1.7571 | 0.7273 | |
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| 0.0783 | 47.0 | 47 | 1.9122 | 0.7273 | |
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| 0.0783 | 48.0 | 48 | 2.1062 | 0.7273 | |
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| 0.0783 | 49.0 | 49 | 2.1661 | 0.7273 | |
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| 0.056 | 50.0 | 50 | 2.1833 | 0.7273 | |
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| 0.056 | 51.0 | 51 | 2.2402 | 0.7273 | |
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| 0.056 | 52.0 | 52 | 2.3007 | 0.7273 | |
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| 0.056 | 53.0 | 53 | 2.3692 | 0.7273 | |
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| 0.056 | 54.0 | 54 | 2.3821 | 0.7273 | |
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| 0.056 | 55.0 | 55 | 2.2716 | 0.7273 | |
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| 0.056 | 56.0 | 56 | 2.0482 | 0.7273 | |
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| 0.056 | 57.0 | 57 | 1.8783 | 0.7273 | |
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| 0.056 | 58.0 | 58 | 1.7967 | 0.7273 | |
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| 0.056 | 59.0 | 59 | 1.7036 | 0.7273 | |
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| 0.052 | 60.0 | 60 | 1.6389 | 0.7273 | |
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| 0.052 | 61.0 | 61 | 1.6354 | 0.8182 | |
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| 0.052 | 62.0 | 62 | 1.6852 | 0.8182 | |
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| 0.052 | 63.0 | 63 | 1.8189 | 0.7273 | |
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| 0.052 | 64.0 | 64 | 1.9683 | 0.7273 | |
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| 0.052 | 65.0 | 65 | 2.0166 | 0.7273 | |
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| 0.052 | 66.0 | 66 | 2.0855 | 0.7273 | |
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| 0.052 | 67.0 | 67 | 2.1359 | 0.7273 | |
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| 0.052 | 68.0 | 68 | 2.2465 | 0.7273 | |
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| 0.052 | 69.0 | 69 | 2.2680 | 0.7273 | |
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| 0.0276 | 70.0 | 70 | 2.2728 | 0.7273 | |
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| 0.0276 | 71.0 | 71 | 2.2820 | 0.7273 | |
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| 0.0276 | 72.0 | 72 | 2.2427 | 0.7273 | |
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| 0.0276 | 73.0 | 73 | 2.2066 | 0.7273 | |
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| 0.0276 | 74.0 | 74 | 2.2434 | 0.7273 | |
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| 0.0276 | 75.0 | 75 | 2.3206 | 0.7273 | |
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| 0.0276 | 76.0 | 76 | 2.4408 | 0.7273 | |
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| 0.0276 | 77.0 | 77 | 2.4810 | 0.7273 | |
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| 0.0276 | 78.0 | 78 | 2.5091 | 0.7273 | |
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| 0.0276 | 79.0 | 79 | 2.4862 | 0.7273 | |
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| 0.0411 | 80.0 | 80 | 2.4502 | 0.7273 | |
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| 0.0411 | 81.0 | 81 | 2.4204 | 0.7273 | |
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| 0.0411 | 82.0 | 82 | 2.3838 | 0.7273 | |
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| 0.0411 | 83.0 | 83 | 2.3431 | 0.7273 | |
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| 0.0411 | 84.0 | 84 | 2.2927 | 0.7273 | |
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| 0.0411 | 85.0 | 85 | 2.2181 | 0.7273 | |
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| 0.0411 | 86.0 | 86 | 2.1633 | 0.7273 | |
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| 0.0411 | 87.0 | 87 | 2.0966 | 0.7273 | |
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| 0.0411 | 88.0 | 88 | 2.0536 | 0.7273 | |
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| 0.0411 | 89.0 | 89 | 2.0427 | 0.7273 | |
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| 0.0317 | 90.0 | 90 | 2.0524 | 0.7273 | |
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| 0.0317 | 91.0 | 91 | 2.0489 | 0.7273 | |
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| 0.0317 | 92.0 | 92 | 2.0648 | 0.7273 | |
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| 0.0317 | 93.0 | 93 | 2.0946 | 0.7273 | |
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| 0.0317 | 94.0 | 94 | 2.1155 | 0.7273 | |
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| 0.0317 | 95.0 | 95 | 2.1469 | 0.7273 | |
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| 0.0317 | 96.0 | 96 | 2.1768 | 0.7273 | |
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| 0.0317 | 97.0 | 97 | 2.2026 | 0.7273 | |
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| 0.0317 | 98.0 | 98 | 2.2205 | 0.7273 | |
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| 0.0317 | 99.0 | 99 | 2.2304 | 0.7273 | |
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| 0.0394 | 100.0 | 100 | 2.2350 | 0.7273 | |
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### Framework versions |
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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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