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CristianR8
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resnet-50-cocoa

Image Classification
Transformers
TensorBoard
Safetensors
efficientnet
vision
Generated from Trainer
Inference Endpoints
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  1. README.md +110 -17
  2. model.safetensors +1 -1
README.md CHANGED
@@ -1,10 +1,8 @@
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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: microsoft/resnet-50
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  tags:
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- - image-classification
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- - vision
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  - generated_from_trainer
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  metrics:
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  - accuracy
@@ -18,10 +16,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # resnet-50-cocoa
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- This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the SemilleroCV/Cocoa-dataset dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.8971
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- - Accuracy: 0.8628
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  ## Model description
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@@ -46,22 +44,117 @@ The following hyperparameters were used during training:
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  - seed: 1337
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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- - num_epochs: 5.0
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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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- | 1.4094 | 1.0 | 196 | 1.4768 | 0.8592 |
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- | 1.0664 | 2.0 | 392 | 1.2090 | 0.8628 |
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- | 1.0295 | 3.0 | 588 | 0.9924 | 0.8628 |
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- | 0.8401 | 4.0 | 784 | 0.9143 | 0.8628 |
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- | 0.8213 | 5.0 | 980 | 0.8971 | 0.8628 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions
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- - Transformers 4.47.0.dev0
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- - Pytorch 2.3.0+cu118
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  - Datasets 3.1.0
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- - Tokenizers 0.20.3
 
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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: google/efficientnet-b0
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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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  # resnet-50-cocoa
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+ This model is a fine-tuned version of [google/efficientnet-b0](https://huggingface.co/google/efficientnet-b0) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3624
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+ - Accuracy: 0.8809
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  ## Model description
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  - seed: 1337
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - num_epochs: 100.0
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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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+ | 1.0627 | 1.0 | 196 | 1.5223 | 0.5596 |
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+ | 0.591 | 2.0 | 392 | 0.8975 | 0.8303 |
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+ | 0.6623 | 3.0 | 588 | 0.6564 | 0.8773 |
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+ | 0.4874 | 4.0 | 784 | 0.6842 | 0.8339 |
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+ | 0.4671 | 5.0 | 980 | 0.4894 | 0.8809 |
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+ | 0.5623 | 6.0 | 1176 | 0.4160 | 0.8736 |
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+ | 0.3917 | 7.0 | 1372 | 0.4022 | 0.8845 |
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+ | 0.3153 | 8.0 | 1568 | 0.4939 | 0.8412 |
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+ | 0.5814 | 9.0 | 1764 | 0.3540 | 0.8773 |
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+ | 0.5883 | 10.0 | 1960 | 0.3493 | 0.8953 |
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+ | 0.4616 | 11.0 | 2156 | 0.7928 | 0.7762 |
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+ | 0.499 | 12.0 | 2352 | 2.0659 | 0.2960 |
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+ | 0.2236 | 13.0 | 2548 | 0.4444 | 0.8520 |
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+ | 0.2083 | 14.0 | 2744 | 0.4640 | 0.8736 |
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+ | 0.3408 | 15.0 | 2940 | 0.3775 | 0.8773 |
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+ | 0.3529 | 16.0 | 3136 | 0.3519 | 0.8881 |
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+ | 0.3859 | 17.0 | 3332 | 0.3310 | 0.9061 |
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+ | 0.3557 | 18.0 | 3528 | 0.3475 | 0.8917 |
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+ | 0.4979 | 19.0 | 3724 | 0.3839 | 0.8592 |
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+ | 0.7133 | 20.0 | 3920 | 0.3032 | 0.9134 |
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+ | 0.4489 | 21.0 | 4116 | 0.4246 | 0.8520 |
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+ | 0.2605 | 22.0 | 4312 | 0.2951 | 0.8989 |
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+ | 0.3787 | 23.0 | 4508 | 0.4357 | 0.8520 |
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+ | 0.3015 | 24.0 | 4704 | 0.3990 | 0.8917 |
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+ | 0.1965 | 25.0 | 4900 | 0.3536 | 0.9097 |
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+ | 0.3903 | 26.0 | 5096 | 0.4166 | 0.8592 |
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+ | 0.1902 | 27.0 | 5292 | 0.4354 | 0.8520 |
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+ | 0.2089 | 28.0 | 5488 | 0.4089 | 0.8592 |
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+ | 0.3574 | 29.0 | 5684 | 0.4787 | 0.8231 |
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+ | 0.3532 | 30.0 | 5880 | 0.3165 | 0.9097 |
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+ | 0.2967 | 31.0 | 6076 | 0.3105 | 0.9134 |
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+ | 0.2364 | 32.0 | 6272 | 0.3560 | 0.9061 |
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+ | 0.3136 | 33.0 | 6468 | 0.2657 | 0.9097 |
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+ | 0.4061 | 34.0 | 6664 | 0.2680 | 0.9134 |
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+ | 0.3296 | 35.0 | 6860 | 0.3798 | 0.9061 |
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+ | 0.2905 | 36.0 | 7056 | 0.5098 | 0.8556 |
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+ | 0.2763 | 37.0 | 7252 | 0.4219 | 0.8809 |
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+ | 0.2454 | 38.0 | 7448 | 0.2852 | 0.9134 |
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+ | 0.6077 | 39.0 | 7644 | 0.3603 | 0.8989 |
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+ | 0.1966 | 40.0 | 7840 | 0.3519 | 0.8736 |
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+ | 0.2473 | 41.0 | 8036 | 0.3343 | 0.9025 |
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+ | 0.2795 | 42.0 | 8232 | 0.3384 | 0.9170 |
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+ | 0.1249 | 43.0 | 8428 | 0.4046 | 0.8773 |
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+ | 0.2943 | 44.0 | 8624 | 0.3953 | 0.8917 |
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+ | 0.3002 | 45.0 | 8820 | 0.5003 | 0.8592 |
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+ | 0.1525 | 46.0 | 9016 | 0.3232 | 0.9170 |
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+ | 0.4022 | 47.0 | 9212 | 0.3113 | 0.9170 |
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+ | 0.4994 | 48.0 | 9408 | 0.4494 | 0.8556 |
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+ | 0.6512 | 49.0 | 9604 | 0.3722 | 0.9206 |
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+ | 0.3152 | 50.0 | 9800 | 0.2852 | 0.9097 |
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+ | 0.1165 | 51.0 | 9996 | 0.4138 | 0.8628 |
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+ | 0.216 | 52.0 | 10192 | 0.3413 | 0.8953 |
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+ | 0.1455 | 53.0 | 10388 | 0.3046 | 0.9170 |
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+ | 0.554 | 54.0 | 10584 | 0.2849 | 0.8989 |
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+ | 0.3586 | 55.0 | 10780 | 0.3517 | 0.9134 |
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+ | 0.2239 | 56.0 | 10976 | 0.4538 | 0.9025 |
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+ | 0.1725 | 57.0 | 11172 | 0.4492 | 0.8592 |
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+ | 0.4689 | 58.0 | 11368 | 0.4739 | 0.8628 |
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+ | 0.3565 | 59.0 | 11564 | 0.2831 | 0.9206 |
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+ | 0.2259 | 60.0 | 11760 | 0.3465 | 0.9206 |
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+ | 0.2212 | 61.0 | 11956 | 0.2884 | 0.9314 |
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+ | 0.2648 | 62.0 | 12152 | 0.4875 | 0.8448 |
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+ | 0.3438 | 63.0 | 12348 | 0.3989 | 0.9061 |
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+ | 0.4785 | 64.0 | 12544 | 0.5953 | 0.8520 |
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+ | 0.06 | 65.0 | 12740 | 0.2954 | 0.9278 |
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+ | 0.1965 | 66.0 | 12936 | 0.5033 | 0.8520 |
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+ | 0.3548 | 67.0 | 13132 | 0.4132 | 0.8809 |
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+ | 0.1279 | 68.0 | 13328 | 0.3743 | 0.9170 |
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+ | 0.2879 | 69.0 | 13524 | 0.6423 | 0.7762 |
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+ | 0.1757 | 70.0 | 13720 | 0.5979 | 0.8014 |
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+ | 0.3338 | 71.0 | 13916 | 0.4398 | 0.8989 |
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+ | 0.1604 | 72.0 | 14112 | 0.5634 | 0.8231 |
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+ | 0.1078 | 73.0 | 14308 | 0.6204 | 0.7762 |
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+ | 0.258 | 74.0 | 14504 | 0.3685 | 0.8953 |
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+ | 0.1227 | 75.0 | 14700 | 0.7026 | 0.8159 |
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+ | 0.2257 | 76.0 | 14896 | 0.4048 | 0.9170 |
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+ | 0.1786 | 77.0 | 15092 | 0.4891 | 0.8845 |
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+ | 0.2006 | 78.0 | 15288 | 0.4216 | 0.8773 |
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+ | 0.3144 | 79.0 | 15484 | 0.2721 | 0.8953 |
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+ | 0.1969 | 80.0 | 15680 | 0.4270 | 0.8484 |
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+ | 0.1405 | 81.0 | 15876 | 0.7632 | 0.7834 |
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+ | 0.1427 | 82.0 | 16072 | 0.3249 | 0.9025 |
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+ | 0.2493 | 83.0 | 16268 | 0.3838 | 0.8989 |
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+ | 0.331 | 84.0 | 16464 | 0.3330 | 0.9206 |
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+ | 0.1231 | 85.0 | 16660 | 0.3246 | 0.8700 |
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+ | 0.2781 | 86.0 | 16856 | 0.3710 | 0.8736 |
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+ | 0.7193 | 87.0 | 17052 | 0.3384 | 0.9061 |
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+ | 0.1149 | 88.0 | 17248 | 0.3703 | 0.9097 |
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+ | 0.0269 | 89.0 | 17444 | 0.5013 | 0.8592 |
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+ | 0.0967 | 90.0 | 17640 | 0.3456 | 0.8989 |
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+ | 0.177 | 91.0 | 17836 | 0.3799 | 0.8881 |
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+ | 0.1917 | 92.0 | 18032 | 0.3239 | 0.9061 |
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+ | 0.2082 | 93.0 | 18228 | 0.4861 | 0.8989 |
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+ | 0.3836 | 94.0 | 18424 | 0.4444 | 0.8736 |
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+ | 0.1 | 95.0 | 18620 | 0.3713 | 0.8845 |
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+ | 0.1785 | 96.0 | 18816 | 0.4279 | 0.8303 |
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+ | 0.19 | 97.0 | 19012 | 0.6588 | 0.8412 |
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+ | 0.099 | 98.0 | 19208 | 0.6632 | 0.8267 |
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+ | 0.1467 | 99.0 | 19404 | 0.4642 | 0.8809 |
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+ | 0.2617 | 100.0 | 19600 | 0.3624 | 0.8809 |
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  ### Framework versions
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+ - Transformers 4.48.0.dev0
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+ - Pytorch 2.5.1+cu124
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  - Datasets 3.1.0
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+ - Tokenizers 0.21.0
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