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vit-base-patch16-224-in21k-finetuned-papsmear

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  2. model.safetensors +1 -1
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README.md CHANGED
@@ -4,22 +4,37 @@ 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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  metrics:
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  - accuracy
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  model-index:
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  - name: vit-base-patch16-224-in21k-finetuned-papsmear
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- results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
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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-base-patch16-224-in21k-finetuned-biopsy
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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 an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1092
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- - Accuracy: 0.9732
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  ## Model description
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@@ -51,63 +66,60 @@ The following hyperparameters were used during training:
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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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- | 1.1553 | 1.0 | 42 | 1.0950 | 0.5477 |
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- | 0.7791 | 2.0 | 84 | 0.6486 | 0.8526 |
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- | 0.433 | 3.0 | 126 | 0.3716 | 0.9129 |
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- | 0.3495 | 4.0 | 168 | 0.2869 | 0.9347 |
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- | 0.2556 | 5.0 | 210 | 0.2722 | 0.9280 |
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- | 0.2791 | 6.0 | 252 | 0.2611 | 0.9330 |
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- | 0.2343 | 7.0 | 294 | 0.2377 | 0.9380 |
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- | 0.186 | 8.0 | 336 | 0.2158 | 0.9397 |
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- | 0.1984 | 9.0 | 378 | 0.2222 | 0.9347 |
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- | 0.1751 | 10.0 | 420 | 0.1993 | 0.9514 |
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- | 0.1529 | 11.0 | 462 | 0.2101 | 0.9430 |
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- | 0.1616 | 12.0 | 504 | 0.2543 | 0.9296 |
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- | 0.1404 | 13.0 | 546 | 0.2029 | 0.9397 |
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- | 0.1078 | 14.0 | 588 | 0.2087 | 0.9414 |
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- | 0.1109 | 15.0 | 630 | 0.1381 | 0.9615 |
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- | 0.1072 | 16.0 | 672 | 0.1895 | 0.9414 |
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- | 0.0949 | 17.0 | 714 | 0.1981 | 0.9397 |
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- | 0.0908 | 18.0 | 756 | 0.1608 | 0.9581 |
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- | 0.0809 | 19.0 | 798 | 0.1764 | 0.9581 |
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- | 0.0708 | 20.0 | 840 | 0.1512 | 0.9531 |
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- | 0.0757 | 21.0 | 882 | 0.2027 | 0.9481 |
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- | 0.0919 | 22.0 | 924 | 0.1487 | 0.9615 |
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- | 0.07 | 23.0 | 966 | 0.1667 | 0.9615 |
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- | 0.0629 | 24.0 | 1008 | 0.1904 | 0.9531 |
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- | 0.0584 | 25.0 | 1050 | 0.1521 | 0.9631 |
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- | 0.0666 | 26.0 | 1092 | 0.1326 | 0.9665 |
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- | 0.062 | 27.0 | 1134 | 0.1772 | 0.9564 |
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- | 0.0568 | 28.0 | 1176 | 0.1465 | 0.9564 |
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- | 0.0453 | 29.0 | 1218 | 0.1347 | 0.9682 |
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- | 0.0469 | 30.0 | 1260 | 0.1687 | 0.9631 |
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- | 0.0541 | 31.0 | 1302 | 0.1390 | 0.9715 |
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- | 0.0602 | 32.0 | 1344 | 0.1618 | 0.9615 |
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- | 0.0497 | 33.0 | 1386 | 0.1415 | 0.9615 |
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- | 0.0493 | 34.0 | 1428 | 0.1521 | 0.9631 |
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- | 0.0606 | 35.0 | 1470 | 0.1429 | 0.9698 |
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- | 0.0332 | 36.0 | 1512 | 0.1671 | 0.9648 |
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- | 0.0432 | 37.0 | 1554 | 0.1441 | 0.9665 |
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- | 0.0354 | 38.0 | 1596 | 0.1593 | 0.9682 |
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- | 0.0432 | 39.0 | 1638 | 0.1395 | 0.9665 |
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- | 0.0363 | 40.0 | 1680 | 0.1092 | 0.9732 |
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- | 0.0288 | 41.0 | 1722 | 0.1550 | 0.9665 |
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- | 0.0305 | 42.0 | 1764 | 0.1462 | 0.9682 |
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- | 0.0326 | 43.0 | 1806 | 0.1343 | 0.9682 |
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- | 0.027 | 44.0 | 1848 | 0.1109 | 0.9732 |
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- | 0.0233 | 45.0 | 1890 | 0.1315 | 0.9732 |
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- | 0.042 | 46.0 | 1932 | 0.1261 | 0.9732 |
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- | 0.0251 | 47.0 | 1974 | 0.1320 | 0.9732 |
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- | 0.041 | 48.0 | 2016 | 0.1282 | 0.9732 |
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- | 0.0445 | 49.0 | 2058 | 0.1296 | 0.9732 |
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- | 0.0308 | 50.0 | 2100 | 0.1325 | 0.9732 |
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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 3.0.1
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- - Tokenizers 0.19.1
 
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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-base-patch16-224-in21k-finetuned-papsmear
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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: 0.9191176470588235
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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-base-patch16-224-in21k-finetuned-papsmear
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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: 0.2707
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+ - Accuracy: 0.9191
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  ## Model description
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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 | 9 | 1.7346 | 0.2647 |
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+ | 1.7645 | 1.9487 | 19 | 1.6152 | 0.3088 |
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+ | 1.661 | 2.9744 | 29 | 1.4663 | 0.4118 |
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+ | 1.496 | 4.0 | 39 | 1.2989 | 0.4853 |
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+ | 1.3097 | 4.9231 | 48 | 1.1491 | 0.5588 |
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+ | 1.091 | 5.9487 | 58 | 0.9933 | 0.7206 |
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+ | 0.9088 | 6.9744 | 68 | 0.9171 | 0.6985 |
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+ | 0.7858 | 8.0 | 78 | 0.8301 | 0.7721 |
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+ | 0.7016 | 8.9231 | 87 | 0.7925 | 0.7353 |
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+ | 0.6136 | 9.9487 | 97 | 0.6992 | 0.7647 |
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+ | 0.532 | 10.9744 | 107 | 0.6401 | 0.8309 |
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+ | 0.5018 | 12.0 | 117 | 0.5787 | 0.8382 |
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+ | 0.4279 | 12.9231 | 126 | 0.6130 | 0.8088 |
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+ | 0.4116 | 13.9487 | 136 | 0.5090 | 0.8382 |
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+ | 0.3848 | 14.9744 | 146 | 0.5165 | 0.8676 |
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+ | 0.3449 | 16.0 | 156 | 0.4843 | 0.8382 |
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+ | 0.3008 | 16.9231 | 165 | 0.5460 | 0.8456 |
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+ | 0.2797 | 17.9487 | 175 | 0.4985 | 0.8309 |
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+ | 0.2696 | 18.9744 | 185 | 0.5586 | 0.8456 |
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+ | 0.2633 | 20.0 | 195 | 0.4349 | 0.9044 |
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+ | 0.2569 | 20.9231 | 204 | 0.4017 | 0.8897 |
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+ | 0.27 | 21.9487 | 214 | 0.4758 | 0.8603 |
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+ | 0.2706 | 22.9744 | 224 | 0.4133 | 0.8897 |
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+ | 0.2211 | 24.0 | 234 | 0.3844 | 0.9118 |
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+ | 0.1977 | 24.9231 | 243 | 0.3497 | 0.9265 |
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+ | 0.1969 | 25.9487 | 253 | 0.3736 | 0.9044 |
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+ | 0.1776 | 26.9744 | 263 | 0.3797 | 0.9044 |
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+ | 0.1787 | 28.0 | 273 | 0.3949 | 0.8897 |
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+ | 0.18 | 28.9231 | 282 | 0.3278 | 0.9265 |
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+ | 0.1797 | 29.9487 | 292 | 0.3615 | 0.9044 |
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+ | 0.1665 | 30.9744 | 302 | 0.4174 | 0.8603 |
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+ | 0.163 | 32.0 | 312 | 0.3574 | 0.8971 |
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+ | 0.1498 | 32.9231 | 321 | 0.3591 | 0.9044 |
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+ | 0.1405 | 33.9487 | 331 | 0.3017 | 0.9191 |
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+ | 0.155 | 34.9744 | 341 | 0.3303 | 0.9265 |
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+ | 0.1519 | 36.0 | 351 | 0.3559 | 0.8971 |
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+ | 0.1415 | 36.9231 | 360 | 0.2890 | 0.9191 |
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+ | 0.1256 | 37.9487 | 370 | 0.3445 | 0.8897 |
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+ | 0.1217 | 38.9744 | 380 | 0.3435 | 0.9118 |
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+ | 0.1285 | 40.0 | 390 | 0.3025 | 0.9191 |
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+ | 0.1285 | 40.9231 | 399 | 0.3602 | 0.8824 |
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+ | 0.1301 | 41.9487 | 409 | 0.3336 | 0.8897 |
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+ | 0.1243 | 42.9744 | 419 | 0.2825 | 0.9338 |
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+ | 0.1191 | 44.0 | 429 | 0.2835 | 0.9265 |
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+ | 0.1221 | 44.9231 | 438 | 0.2724 | 0.9191 |
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+ | 0.1151 | 45.9487 | 448 | 0.2708 | 0.9191 |
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+ | 0.1195 | 46.1538 | 450 | 0.2707 | 0.9191 |
 
 
 
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  ### Framework versions
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+ - Transformers 4.45.2
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  - Pytorch 2.4.1+cu121
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+ - Datasets 3.0.2
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+ - Tokenizers 0.20.1
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