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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test_7 |
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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-msn-small-lateral_flow_ivalidation_train_test_7 |
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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: test |
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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: 0.8791208791208791 |
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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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# vit-msn-small-lateral_flow_ivalidation_train_test_7 |
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This model is a fine-tuned version of [Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test_7](https://huggingface.co/Melo1512/vit-msn-small-lateral_flow_ivalidation_train_test_7) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4160 |
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- Accuracy: 0.8791 |
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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: 1e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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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: 0.2 |
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- num_epochs: 100 |
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- label_smoothing_factor: 0.1 |
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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 | 0.9231 | 3 | 0.4160 | 0.8791 | |
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| No log | 1.8462 | 6 | 0.4668 | 0.8388 | |
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| No log | 2.7692 | 9 | 0.5433 | 0.8022 | |
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| 0.3869 | 4.0 | 13 | 0.5052 | 0.8168 | |
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| 0.3869 | 4.9231 | 16 | 0.4591 | 0.8571 | |
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| 0.3869 | 5.8462 | 19 | 0.4820 | 0.8278 | |
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| 0.3658 | 6.7692 | 22 | 0.4953 | 0.8095 | |
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| 0.3658 | 8.0 | 26 | 0.4497 | 0.8608 | |
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| 0.3658 | 8.9231 | 29 | 0.4686 | 0.8315 | |
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| 0.3439 | 9.8462 | 32 | 0.4506 | 0.8608 | |
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| 0.3439 | 10.7692 | 35 | 0.4859 | 0.8168 | |
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| 0.3439 | 12.0 | 39 | 0.4929 | 0.8168 | |
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| 0.3416 | 12.9231 | 42 | 0.4957 | 0.8059 | |
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| 0.3416 | 13.8462 | 45 | 0.5229 | 0.7875 | |
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| 0.3416 | 14.7692 | 48 | 0.4473 | 0.8535 | |
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| 0.324 | 16.0 | 52 | 0.5260 | 0.8059 | |
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| 0.324 | 16.9231 | 55 | 0.4582 | 0.8462 | |
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| 0.324 | 17.8462 | 58 | 0.5299 | 0.7839 | |
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| 0.3273 | 18.7692 | 61 | 0.4947 | 0.8205 | |
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| 0.3273 | 20.0 | 65 | 0.5393 | 0.7692 | |
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| 0.3273 | 20.9231 | 68 | 0.4916 | 0.8278 | |
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| 0.3397 | 21.8462 | 71 | 0.5360 | 0.7802 | |
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| 0.3397 | 22.7692 | 74 | 0.5661 | 0.7656 | |
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| 0.3397 | 24.0 | 78 | 0.6354 | 0.7216 | |
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| 0.3344 | 24.9231 | 81 | 0.6782 | 0.7033 | |
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| 0.3344 | 25.8462 | 84 | 0.5704 | 0.7582 | |
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| 0.3344 | 26.7692 | 87 | 0.6537 | 0.6777 | |
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| 0.3325 | 28.0 | 91 | 0.4798 | 0.8425 | |
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| 0.3325 | 28.9231 | 94 | 0.5158 | 0.8059 | |
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| 0.3325 | 29.8462 | 97 | 0.5408 | 0.7912 | |
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| 0.3283 | 30.7692 | 100 | 0.5964 | 0.7399 | |
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| 0.3283 | 32.0 | 104 | 0.5069 | 0.8205 | |
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| 0.3283 | 32.9231 | 107 | 0.5396 | 0.7875 | |
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| 0.3229 | 33.8462 | 110 | 0.5203 | 0.7985 | |
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| 0.3229 | 34.7692 | 113 | 0.5464 | 0.7875 | |
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| 0.3229 | 36.0 | 117 | 0.5890 | 0.7509 | |
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| 0.3207 | 36.9231 | 120 | 0.5080 | 0.8132 | |
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| 0.3207 | 37.8462 | 123 | 0.4944 | 0.8168 | |
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| 0.3207 | 38.7692 | 126 | 0.4968 | 0.8095 | |
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| 0.3286 | 40.0 | 130 | 0.4874 | 0.8132 | |
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| 0.3286 | 40.9231 | 133 | 0.5013 | 0.8059 | |
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| 0.3286 | 41.8462 | 136 | 0.5329 | 0.7656 | |
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| 0.3286 | 42.7692 | 139 | 0.6199 | 0.6996 | |
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| 0.3154 | 44.0 | 143 | 0.4854 | 0.8059 | |
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| 0.3154 | 44.9231 | 146 | 0.5545 | 0.7509 | |
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| 0.3154 | 45.8462 | 149 | 0.5267 | 0.7729 | |
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| 0.3119 | 46.7692 | 152 | 0.5214 | 0.7802 | |
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| 0.3119 | 48.0 | 156 | 0.5265 | 0.7839 | |
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| 0.3119 | 48.9231 | 159 | 0.5137 | 0.7985 | |
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| 0.3036 | 49.8462 | 162 | 0.5354 | 0.7839 | |
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| 0.3036 | 50.7692 | 165 | 0.5269 | 0.7875 | |
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| 0.3036 | 52.0 | 169 | 0.5797 | 0.7399 | |
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| 0.2995 | 52.9231 | 172 | 0.6258 | 0.7179 | |
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| 0.2995 | 53.8462 | 175 | 0.5512 | 0.7692 | |
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| 0.2995 | 54.7692 | 178 | 0.5517 | 0.7619 | |
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| 0.306 | 56.0 | 182 | 0.5590 | 0.7546 | |
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| 0.306 | 56.9231 | 185 | 0.5514 | 0.7619 | |
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| 0.306 | 57.8462 | 188 | 0.5597 | 0.7509 | |
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| 0.2989 | 58.7692 | 191 | 0.5957 | 0.7326 | |
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| 0.2989 | 60.0 | 195 | 0.5366 | 0.7766 | |
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| 0.2989 | 60.9231 | 198 | 0.5465 | 0.7729 | |
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| 0.2931 | 61.8462 | 201 | 0.6171 | 0.7253 | |
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| 0.2931 | 62.7692 | 204 | 0.5768 | 0.7509 | |
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| 0.2931 | 64.0 | 208 | 0.5706 | 0.7509 | |
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| 0.299 | 64.9231 | 211 | 0.5962 | 0.7363 | |
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| 0.299 | 65.8462 | 214 | 0.6220 | 0.7216 | |
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| 0.299 | 66.7692 | 217 | 0.5929 | 0.7363 | |
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| 0.2969 | 68.0 | 221 | 0.6136 | 0.7253 | |
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| 0.2969 | 68.9231 | 224 | 0.6092 | 0.7289 | |
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| 0.2969 | 69.8462 | 227 | 0.6029 | 0.7253 | |
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| 0.3015 | 70.7692 | 230 | 0.5356 | 0.7766 | |
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| 0.3015 | 72.0 | 234 | 0.5376 | 0.7692 | |
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| 0.3015 | 72.9231 | 237 | 0.5886 | 0.7436 | |
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| 0.2919 | 73.8462 | 240 | 0.5869 | 0.7436 | |
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| 0.2919 | 74.7692 | 243 | 0.5846 | 0.7473 | |
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| 0.2919 | 76.0 | 247 | 0.5507 | 0.7656 | |
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| 0.288 | 76.9231 | 250 | 0.5801 | 0.7509 | |
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| 0.288 | 77.8462 | 253 | 0.6077 | 0.7399 | |
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| 0.288 | 78.7692 | 256 | 0.5848 | 0.7436 | |
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| 0.2951 | 80.0 | 260 | 0.5435 | 0.7692 | |
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| 0.2951 | 80.9231 | 263 | 0.5638 | 0.7656 | |
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| 0.2951 | 81.8462 | 266 | 0.5795 | 0.7399 | |
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| 0.2951 | 82.7692 | 269 | 0.5774 | 0.7509 | |
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| 0.2875 | 84.0 | 273 | 0.5703 | 0.7509 | |
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| 0.2875 | 84.9231 | 276 | 0.5713 | 0.7509 | |
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| 0.2875 | 85.8462 | 279 | 0.5784 | 0.7473 | |
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| 0.2855 | 86.7692 | 282 | 0.5904 | 0.7436 | |
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| 0.2855 | 88.0 | 286 | 0.5917 | 0.7326 | |
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| 0.2855 | 88.9231 | 289 | 0.5860 | 0.7473 | |
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| 0.2964 | 89.8462 | 292 | 0.5858 | 0.7473 | |
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| 0.2964 | 90.7692 | 295 | 0.5823 | 0.7436 | |
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| 0.2964 | 92.0 | 299 | 0.5817 | 0.7436 | |
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| 0.291 | 92.3077 | 300 | 0.5816 | 0.7436 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.19.1 |
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