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vit-base-patch16-384-finetuned-humid-binary-2

Image Classification
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vit
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README.md ADDED
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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/vit-base-patch16-384
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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-patch16-384-finetuned-humid-binary-2
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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: validation
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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: 0.8970588235294118
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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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+ # vit-base-patch16-384-finetuned-humid-binary-2
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+
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+ This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the imagefolder dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.6924
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+ - Accuracy: 0.8971
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+ - F1 Macro: 0.8965
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+ - Precision Macro: 0.8971
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+ - Recall Macro: 0.9058
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+ - Precision Dry: 0.8235
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+ - Recall Dry: 0.9655
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+ - F1 Dry: 0.8889
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+ - Precision Humid: 0.9706
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+ - Recall Humid: 0.8462
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+ - F1 Humid: 0.9041
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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: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 64
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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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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 50
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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 | F1 Macro | Precision Macro | Recall Macro | Precision Dry | Recall Dry | F1 Dry | Precision Humid | Recall Humid | F1 Humid |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:------------:|:-------------:|:----------:|:------:|:---------------:|:------------:|:--------:|
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+ | No log | 1.0 | 6 | 0.6149 | 0.7206 | 0.7176 | 0.7170 | 0.7210 | 0.6562 | 0.7241 | 0.6885 | 0.7778 | 0.7179 | 0.7467 |
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+ | 0.6101 | 2.0 | 12 | 0.3865 | 0.8824 | 0.8798 | 0.8798 | 0.8798 | 0.8621 | 0.8621 | 0.8621 | 0.8974 | 0.8974 | 0.8974 |
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+ | 0.6101 | 3.0 | 18 | 0.3597 | 0.8382 | 0.8379 | 0.8438 | 0.8501 | 0.75 | 0.9310 | 0.8308 | 0.9375 | 0.7692 | 0.8451 |
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+ | 0.2613 | 4.0 | 24 | 0.5014 | 0.8235 | 0.8235 | 0.8417 | 0.8417 | 0.7179 | 0.9655 | 0.8235 | 0.9655 | 0.7179 | 0.8235 |
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+ | 0.1505 | 5.0 | 30 | 0.4383 | 0.8529 | 0.8524 | 0.8554 | 0.8630 | 0.7714 | 0.9310 | 0.8438 | 0.9394 | 0.7949 | 0.8611 |
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+ | 0.1505 | 6.0 | 36 | 0.4248 | 0.8529 | 0.8528 | 0.8622 | 0.8674 | 0.7568 | 0.9655 | 0.8485 | 0.9677 | 0.7692 | 0.8571 |
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+ | 0.1298 | 7.0 | 42 | 0.3932 | 0.8971 | 0.8932 | 0.9020 | 0.8882 | 0.9231 | 0.8276 | 0.8727 | 0.8810 | 0.9487 | 0.9136 |
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+ | 0.1298 | 8.0 | 48 | 0.3933 | 0.8971 | 0.8960 | 0.8941 | 0.9014 | 0.8438 | 0.9310 | 0.8852 | 0.9444 | 0.8718 | 0.9067 |
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+ | 0.1033 | 9.0 | 54 | 0.7160 | 0.7941 | 0.7934 | 0.8372 | 0.8205 | 0.6744 | 1.0 | 0.8056 | 1.0 | 0.6410 | 0.7812 |
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+ | 0.1135 | 10.0 | 60 | 0.4036 | 0.9265 | 0.9252 | 0.9237 | 0.9271 | 0.9 | 0.9310 | 0.9153 | 0.9474 | 0.9231 | 0.9351 |
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+ | 0.1135 | 11.0 | 66 | 0.4228 | 0.9118 | 0.9089 | 0.9142 | 0.9054 | 0.9259 | 0.8621 | 0.8929 | 0.9024 | 0.9487 | 0.925 |
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+ | 0.0331 | 12.0 | 72 | 0.4821 | 0.8824 | 0.8819 | 0.8848 | 0.8930 | 0.8 | 0.9655 | 0.875 | 0.9697 | 0.8205 | 0.8889 |
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+ | 0.0331 | 13.0 | 78 | 0.4474 | 0.9118 | 0.9105 | 0.9085 | 0.9142 | 0.8710 | 0.9310 | 0.9 | 0.9459 | 0.8974 | 0.9211 |
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+ | 0.0142 | 14.0 | 84 | 0.4478 | 0.9118 | 0.9098 | 0.9098 | 0.9098 | 0.8966 | 0.8966 | 0.8966 | 0.9231 | 0.9231 | 0.9231 |
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+ | 0.0074 | 15.0 | 90 | 0.6302 | 0.8824 | 0.8819 | 0.8848 | 0.8930 | 0.8 | 0.9655 | 0.875 | 0.9697 | 0.8205 | 0.8889 |
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+ | 0.0074 | 16.0 | 96 | 0.4611 | 0.9412 | 0.9399 | 0.9399 | 0.9399 | 0.9310 | 0.9310 | 0.9310 | 0.9487 | 0.9487 | 0.9487 |
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+ | 0.0114 | 17.0 | 102 | 0.6472 | 0.8824 | 0.8819 | 0.8848 | 0.8930 | 0.8 | 0.9655 | 0.875 | 0.9697 | 0.8205 | 0.8889 |
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+ | 0.0114 | 18.0 | 108 | 0.4690 | 0.9118 | 0.9089 | 0.9142 | 0.9054 | 0.9259 | 0.8621 | 0.8929 | 0.9024 | 0.9487 | 0.925 |
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+ | 0.0054 | 19.0 | 114 | 0.7610 | 0.8676 | 0.8674 | 0.8733 | 0.8802 | 0.7778 | 0.9655 | 0.8615 | 0.9688 | 0.7949 | 0.8732 |
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+ | 0.0024 | 20.0 | 120 | 0.5395 | 0.9118 | 0.9105 | 0.9085 | 0.9142 | 0.8710 | 0.9310 | 0.9 | 0.9459 | 0.8974 | 0.9211 |
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+ | 0.0024 | 21.0 | 126 | 0.6275 | 0.8971 | 0.8960 | 0.8941 | 0.9014 | 0.8438 | 0.9310 | 0.8852 | 0.9444 | 0.8718 | 0.9067 |
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+ | 0.0024 | 22.0 | 132 | 0.7972 | 0.8676 | 0.8674 | 0.8733 | 0.8802 | 0.7778 | 0.9655 | 0.8615 | 0.9688 | 0.7949 | 0.8732 |
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+ | 0.0024 | 23.0 | 138 | 0.5466 | 0.9118 | 0.9105 | 0.9085 | 0.9142 | 0.8710 | 0.9310 | 0.9 | 0.9459 | 0.8974 | 0.9211 |
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+ | 0.0009 | 24.0 | 144 | 0.5304 | 0.8971 | 0.8960 | 0.8941 | 0.9014 | 0.8438 | 0.9310 | 0.8852 | 0.9444 | 0.8718 | 0.9067 |
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+ | 0.0012 | 25.0 | 150 | 0.6568 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0012 | 26.0 | 156 | 0.6134 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0004 | 27.0 | 162 | 0.6094 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0004 | 28.0 | 168 | 0.6153 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0003 | 29.0 | 174 | 0.6319 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0002 | 30.0 | 180 | 0.6457 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0002 | 31.0 | 186 | 0.6530 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0002 | 32.0 | 192 | 0.6580 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0002 | 33.0 | 198 | 0.6650 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0002 | 34.0 | 204 | 0.6774 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0002 | 35.0 | 210 | 0.6827 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0002 | 36.0 | 216 | 0.6850 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0001 | 37.0 | 222 | 0.6846 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0001 | 38.0 | 228 | 0.6853 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0001 | 39.0 | 234 | 0.6854 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0001 | 40.0 | 240 | 0.6845 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0001 | 41.0 | 246 | 0.6847 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0001 | 42.0 | 252 | 0.6860 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0001 | 43.0 | 258 | 0.6875 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0001 | 44.0 | 264 | 0.6895 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0001 | 45.0 | 270 | 0.6903 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0001 | 46.0 | 276 | 0.6914 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0001 | 47.0 | 282 | 0.6920 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0001 | 48.0 | 288 | 0.6921 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0001 | 49.0 | 294 | 0.6923 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+ | 0.0001 | 50.0 | 300 | 0.6924 | 0.8971 | 0.8965 | 0.8971 | 0.9058 | 0.8235 | 0.9655 | 0.8889 | 0.9706 | 0.8462 | 0.9041 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.56.1
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+ - Pytorch 2.5.1+cu124
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+ - Datasets 4.0.0
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+ - Tokenizers 0.22.0
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