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PushkarA07
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segformer-b0-finetuned-batch3-19May

Image Segmentation
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segformer
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Generated from Trainer
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  4. training_args.bin +3 -0
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  ---
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  library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- ## How to Get Started with the Model
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- ## Training Details
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- ### Training Data
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- ### Training Procedure
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- #### Preprocessing [optional]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- ## Evaluation
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- ## Technical Specifications [optional]
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1
  ---
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  library_name: transformers
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+ license: other
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+ base_model: PushkarA07/segformer-b0-finetuned-batch2w5-15Dec
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+ tags:
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+ - vision
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+ - image-segmentation
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+ - generated_from_trainer
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+ model-index:
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+ - name: segformer-b0-finetuned-batch3-19May
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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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+
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+ # segformer-b0-finetuned-batch3-19May
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+
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+ This model is a fine-tuned version of [PushkarA07/segformer-b0-finetuned-batch2w5-15Dec](https://huggingface.co/PushkarA07/segformer-b0-finetuned-batch2w5-15Dec) on the PushkarA07/batch3-tiles_first dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0057
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+ - Mean Iou: 0.7819
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+ - Mean Accuracy: 0.8573
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+ - Overall Accuracy: 0.9984
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+ - Accuracy Abnormality: 0.7153
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+ - Iou Abnormality: 0.5654
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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: 6e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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+ - seed: 42
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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
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Abnormality | Iou Abnormality |
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+ |:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------------:|:---------------:|
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+ | 0.005 | 0.9091 | 10 | 0.0059 | 0.7411 | 0.8270 | 0.9980 | 0.6550 | 0.4843 |
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+ | 0.0038 | 1.8182 | 20 | 0.0061 | 0.7456 | 0.8639 | 0.9978 | 0.7293 | 0.4935 |
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+ | 0.004 | 2.7273 | 30 | 0.0053 | 0.7557 | 0.8153 | 0.9983 | 0.6312 | 0.5132 |
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+ | 0.0041 | 3.6364 | 40 | 0.0053 | 0.7559 | 0.8268 | 0.9982 | 0.6544 | 0.5137 |
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+ | 0.0051 | 4.5455 | 50 | 0.0051 | 0.7659 | 0.8514 | 0.9982 | 0.7037 | 0.5335 |
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+ | 0.0022 | 5.4545 | 60 | 0.0049 | 0.7669 | 0.8381 | 0.9983 | 0.6770 | 0.5356 |
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+ | 0.0023 | 6.3636 | 70 | 0.0052 | 0.7667 | 0.8571 | 0.9982 | 0.7153 | 0.5353 |
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+ | 0.0016 | 7.2727 | 80 | 0.0052 | 0.7679 | 0.8263 | 0.9984 | 0.6533 | 0.5375 |
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+ | 0.0025 | 8.1818 | 90 | 0.0054 | 0.7687 | 0.8682 | 0.9982 | 0.7374 | 0.5392 |
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+ | 0.0045 | 9.0909 | 100 | 0.0060 | 0.7622 | 0.8849 | 0.9980 | 0.7712 | 0.5264 |
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+ | 0.0048 | 10.0 | 110 | 0.0054 | 0.7609 | 0.8045 | 0.9984 | 0.6094 | 0.5235 |
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+ | 0.0035 | 10.9091 | 120 | 0.0054 | 0.7626 | 0.8587 | 0.9981 | 0.7185 | 0.5271 |
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+ | 0.0033 | 11.8182 | 130 | 0.0051 | 0.7700 | 0.8375 | 0.9983 | 0.6758 | 0.5417 |
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+ | 0.0022 | 12.7273 | 140 | 0.0052 | 0.7691 | 0.8476 | 0.9983 | 0.6961 | 0.5400 |
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+ | 0.0039 | 13.6364 | 150 | 0.0055 | 0.7639 | 0.8710 | 0.9981 | 0.7432 | 0.5296 |
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+ | 0.0034 | 14.5455 | 160 | 0.0051 | 0.7740 | 0.8457 | 0.9984 | 0.6922 | 0.5497 |
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+ | 0.0039 | 15.4545 | 170 | 0.0053 | 0.7718 | 0.8653 | 0.9982 | 0.7316 | 0.5453 |
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+ | 0.0024 | 16.3636 | 180 | 0.0053 | 0.7733 | 0.8620 | 0.9983 | 0.7248 | 0.5484 |
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+ | 0.0023 | 17.2727 | 190 | 0.0051 | 0.7756 | 0.8424 | 0.9984 | 0.6855 | 0.5527 |
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+ | 0.0022 | 18.1818 | 200 | 0.0053 | 0.7745 | 0.8748 | 0.9982 | 0.7507 | 0.5507 |
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+ | 0.0041 | 19.0909 | 210 | 0.0051 | 0.7755 | 0.8470 | 0.9984 | 0.6947 | 0.5527 |
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+ | 0.0025 | 20.0 | 220 | 0.0052 | 0.7791 | 0.8638 | 0.9983 | 0.7284 | 0.5599 |
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+ | 0.0029 | 20.9091 | 230 | 0.0052 | 0.7760 | 0.8554 | 0.9983 | 0.7117 | 0.5537 |
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+ | 0.0007 | 21.8182 | 240 | 0.0052 | 0.7760 | 0.8506 | 0.9984 | 0.7020 | 0.5537 |
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+ | 0.0026 | 22.7273 | 250 | 0.0052 | 0.7782 | 0.8627 | 0.9983 | 0.7263 | 0.5581 |
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+ | 0.0026 | 23.6364 | 260 | 0.0051 | 0.7791 | 0.8471 | 0.9984 | 0.6950 | 0.5597 |
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+ | 0.0015 | 24.5455 | 270 | 0.0053 | 0.7754 | 0.8665 | 0.9983 | 0.7339 | 0.5525 |
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+ | 0.0044 | 25.4545 | 280 | 0.0053 | 0.7773 | 0.8562 | 0.9983 | 0.7133 | 0.5563 |
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+ | 0.0023 | 26.3636 | 290 | 0.0053 | 0.7790 | 0.8606 | 0.9984 | 0.7220 | 0.5597 |
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+ | 0.0021 | 27.2727 | 300 | 0.0055 | 0.7769 | 0.8735 | 0.9983 | 0.7480 | 0.5556 |
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+ | 0.0024 | 28.1818 | 310 | 0.0051 | 0.7809 | 0.8514 | 0.9984 | 0.7035 | 0.5633 |
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+ | 0.0017 | 29.0909 | 320 | 0.0055 | 0.7786 | 0.8643 | 0.9983 | 0.7294 | 0.5589 |
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+ | 0.003 | 30.0 | 330 | 0.0053 | 0.7777 | 0.8528 | 0.9984 | 0.7063 | 0.5570 |
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+ | 0.0012 | 30.9091 | 340 | 0.0052 | 0.7760 | 0.8360 | 0.9984 | 0.6727 | 0.5535 |
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+ | 0.003 | 31.8182 | 350 | 0.0054 | 0.7813 | 0.8648 | 0.9984 | 0.7305 | 0.5642 |
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+ | 0.0043 | 32.7273 | 360 | 0.0052 | 0.7813 | 0.8465 | 0.9984 | 0.6937 | 0.5642 |
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+ | 0.0032 | 33.6364 | 370 | 0.0052 | 0.7835 | 0.8587 | 0.9984 | 0.7181 | 0.5685 |
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+ | 0.0025 | 34.5455 | 380 | 0.0053 | 0.7837 | 0.8637 | 0.9984 | 0.7281 | 0.5691 |
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+ | 0.0017 | 35.4545 | 390 | 0.0051 | 0.7825 | 0.8380 | 0.9985 | 0.6766 | 0.5666 |
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+ | 0.0024 | 36.3636 | 400 | 0.0055 | 0.7788 | 0.8704 | 0.9983 | 0.7418 | 0.5594 |
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+ | 0.0026 | 37.2727 | 410 | 0.0053 | 0.7813 | 0.8533 | 0.9984 | 0.7073 | 0.5641 |
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+ | 0.0023 | 38.1818 | 420 | 0.0051 | 0.7845 | 0.8646 | 0.9984 | 0.7300 | 0.5706 |
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+ | 0.0023 | 39.0909 | 430 | 0.0053 | 0.7805 | 0.8648 | 0.9984 | 0.7304 | 0.5627 |
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+ | 0.0021 | 40.0 | 440 | 0.0053 | 0.7823 | 0.8541 | 0.9984 | 0.7089 | 0.5662 |
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+ | 0.0027 | 40.9091 | 450 | 0.0053 | 0.7834 | 0.8583 | 0.9984 | 0.7174 | 0.5683 |
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+ | 0.004 | 41.8182 | 460 | 0.0052 | 0.7854 | 0.8545 | 0.9985 | 0.7096 | 0.5724 |
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+ | 0.003 | 42.7273 | 470 | 0.0053 | 0.7826 | 0.8466 | 0.9985 | 0.6939 | 0.5668 |
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+ | 0.0035 | 43.6364 | 480 | 0.0054 | 0.7815 | 0.8637 | 0.9984 | 0.7282 | 0.5646 |
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+ | 0.0033 | 44.5455 | 490 | 0.0053 | 0.7802 | 0.8560 | 0.9984 | 0.7127 | 0.5620 |
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+ | 0.0027 | 45.4545 | 500 | 0.0051 | 0.7828 | 0.8489 | 0.9985 | 0.6985 | 0.5672 |
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+ | 0.0032 | 46.3636 | 510 | 0.0053 | 0.7836 | 0.8605 | 0.9984 | 0.7218 | 0.5687 |
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+ | 0.0034 | 47.2727 | 520 | 0.0054 | 0.7830 | 0.8521 | 0.9984 | 0.7049 | 0.5675 |
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+ | 0.0017 | 48.1818 | 530 | 0.0054 | 0.7833 | 0.8595 | 0.9984 | 0.7198 | 0.5681 |
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+ | 0.003 | 49.0909 | 540 | 0.0054 | 0.7809 | 0.8509 | 0.9984 | 0.7024 | 0.5633 |
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+ | 0.0013 | 50.0 | 550 | 0.0053 | 0.7841 | 0.8533 | 0.9985 | 0.7073 | 0.5697 |
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+ | 0.0026 | 50.9091 | 560 | 0.0054 | 0.7828 | 0.8589 | 0.9984 | 0.7186 | 0.5671 |
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+ | 0.0013 | 51.8182 | 570 | 0.0054 | 0.7831 | 0.8552 | 0.9984 | 0.7111 | 0.5677 |
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+ | 0.0019 | 52.7273 | 580 | 0.0055 | 0.7808 | 0.8645 | 0.9984 | 0.7298 | 0.5632 |
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+ | 0.0024 | 53.6364 | 590 | 0.0054 | 0.7828 | 0.8550 | 0.9984 | 0.7107 | 0.5671 |
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+ | 0.0024 | 54.5455 | 600 | 0.0054 | 0.7837 | 0.8593 | 0.9984 | 0.7193 | 0.5690 |
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+ | 0.0025 | 55.4545 | 610 | 0.0055 | 0.7818 | 0.8566 | 0.9984 | 0.7140 | 0.5653 |
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+ | 0.0018 | 56.3636 | 620 | 0.0054 | 0.7846 | 0.8509 | 0.9985 | 0.7025 | 0.5707 |
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+ | 0.0027 | 57.2727 | 630 | 0.0054 | 0.7830 | 0.8571 | 0.9984 | 0.7149 | 0.5675 |
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+ | 0.0017 | 58.1818 | 640 | 0.0054 | 0.7833 | 0.8575 | 0.9984 | 0.7158 | 0.5682 |
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+ | 0.0038 | 59.0909 | 650 | 0.0054 | 0.7855 | 0.8585 | 0.9984 | 0.7178 | 0.5725 |
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+ | 0.0018 | 60.0 | 660 | 0.0058 | 0.7780 | 0.8628 | 0.9983 | 0.7266 | 0.5576 |
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+ | 0.0023 | 60.9091 | 670 | 0.0056 | 0.7809 | 0.8534 | 0.9984 | 0.7075 | 0.5634 |
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+ | 0.002 | 61.8182 | 680 | 0.0055 | 0.7841 | 0.8549 | 0.9984 | 0.7105 | 0.5698 |
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+ | 0.0038 | 62.7273 | 690 | 0.0055 | 0.7822 | 0.8562 | 0.9984 | 0.7132 | 0.5661 |
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+ | 0.0024 | 63.6364 | 700 | 0.0055 | 0.7813 | 0.8556 | 0.9984 | 0.7120 | 0.5642 |
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+ | 0.0027 | 64.5455 | 710 | 0.0055 | 0.7828 | 0.8597 | 0.9984 | 0.7202 | 0.5672 |
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+ | 0.0019 | 65.4545 | 720 | 0.0056 | 0.7813 | 0.8562 | 0.9984 | 0.7131 | 0.5642 |
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+ | 0.0018 | 66.3636 | 730 | 0.0056 | 0.7818 | 0.8595 | 0.9984 | 0.7199 | 0.5653 |
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+ | 0.0034 | 67.2727 | 740 | 0.0055 | 0.7825 | 0.8560 | 0.9984 | 0.7127 | 0.5666 |
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+ | 0.0021 | 68.1818 | 750 | 0.0056 | 0.7818 | 0.8540 | 0.9984 | 0.7087 | 0.5652 |
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+ | 0.0014 | 69.0909 | 760 | 0.0056 | 0.7818 | 0.8542 | 0.9984 | 0.7091 | 0.5652 |
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+ | 0.002 | 70.0 | 770 | 0.0056 | 0.7819 | 0.8600 | 0.9984 | 0.7207 | 0.5654 |
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+ | 0.0009 | 70.9091 | 780 | 0.0057 | 0.7814 | 0.8586 | 0.9984 | 0.7181 | 0.5645 |
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+ | 0.0014 | 71.8182 | 790 | 0.0054 | 0.7836 | 0.8604 | 0.9984 | 0.7216 | 0.5688 |
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+ | 0.0014 | 72.7273 | 800 | 0.0057 | 0.7799 | 0.8571 | 0.9984 | 0.7150 | 0.5615 |
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+ | 0.0036 | 73.6364 | 810 | 0.0058 | 0.7796 | 0.8603 | 0.9984 | 0.7214 | 0.5607 |
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+ | 0.0024 | 74.5455 | 820 | 0.0058 | 0.7787 | 0.8648 | 0.9983 | 0.7304 | 0.5590 |
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+ | 0.0036 | 75.4545 | 830 | 0.0058 | 0.7786 | 0.8580 | 0.9984 | 0.7168 | 0.5589 |
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+ | 0.0016 | 76.3636 | 840 | 0.0057 | 0.7811 | 0.8626 | 0.9984 | 0.7260 | 0.5639 |
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+ | 0.0023 | 77.2727 | 850 | 0.0057 | 0.7810 | 0.8586 | 0.9984 | 0.7181 | 0.5637 |
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+ | 0.0018 | 78.1818 | 860 | 0.0057 | 0.7808 | 0.8576 | 0.9984 | 0.7161 | 0.5633 |
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+ | 0.0019 | 79.0909 | 870 | 0.0058 | 0.7797 | 0.8637 | 0.9983 | 0.7283 | 0.5610 |
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+ | 0.0022 | 80.0 | 880 | 0.0057 | 0.7817 | 0.8526 | 0.9984 | 0.7058 | 0.5650 |
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+ | 0.0023 | 80.9091 | 890 | 0.0059 | 0.7796 | 0.8617 | 0.9984 | 0.7243 | 0.5608 |
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+ | 0.0019 | 81.8182 | 900 | 0.0058 | 0.7803 | 0.8572 | 0.9984 | 0.7152 | 0.5622 |
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+ | 0.003 | 82.7273 | 910 | 0.0058 | 0.7802 | 0.8557 | 0.9984 | 0.7121 | 0.5619 |
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+ | 0.0024 | 83.6364 | 920 | 0.0058 | 0.7809 | 0.8611 | 0.9984 | 0.7230 | 0.5634 |
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+ | 0.0014 | 84.5455 | 930 | 0.0058 | 0.7817 | 0.8581 | 0.9984 | 0.7169 | 0.5650 |
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+ | 0.0017 | 85.4545 | 940 | 0.0058 | 0.7815 | 0.8587 | 0.9984 | 0.7181 | 0.5645 |
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+ | 0.0031 | 86.3636 | 950 | 0.0058 | 0.7804 | 0.8589 | 0.9984 | 0.7187 | 0.5624 |
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+ | 0.003 | 87.2727 | 960 | 0.0058 | 0.7809 | 0.8568 | 0.9984 | 0.7143 | 0.5634 |
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+ | 0.0007 | 88.1818 | 970 | 0.0058 | 0.7806 | 0.8594 | 0.9984 | 0.7195 | 0.5629 |
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+ | 0.0024 | 89.0909 | 980 | 0.0057 | 0.7821 | 0.8577 | 0.9984 | 0.7162 | 0.5657 |
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+ | 0.0039 | 90.0 | 990 | 0.0057 | 0.7826 | 0.8596 | 0.9984 | 0.7199 | 0.5669 |
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+ | 0.0016 | 90.9091 | 1000 | 0.0057 | 0.7826 | 0.8578 | 0.9984 | 0.7163 | 0.5669 |
157
+ | 0.0024 | 91.8182 | 1010 | 0.0057 | 0.7822 | 0.8574 | 0.9984 | 0.7156 | 0.5659 |
158
+ | 0.0027 | 92.7273 | 1020 | 0.0057 | 0.7817 | 0.8593 | 0.9984 | 0.7195 | 0.5651 |
159
+ | 0.0018 | 93.6364 | 1030 | 0.0057 | 0.7821 | 0.8591 | 0.9984 | 0.7190 | 0.5658 |
160
+ | 0.0012 | 94.5455 | 1040 | 0.0057 | 0.7821 | 0.8581 | 0.9984 | 0.7169 | 0.5659 |
161
+ | 0.0017 | 95.4545 | 1050 | 0.0057 | 0.7816 | 0.8587 | 0.9984 | 0.7181 | 0.5648 |
162
+ | 0.0022 | 96.3636 | 1060 | 0.0057 | 0.7815 | 0.8582 | 0.9984 | 0.7172 | 0.5647 |
163
+ | 0.0019 | 97.2727 | 1070 | 0.0058 | 0.7818 | 0.8580 | 0.9984 | 0.7168 | 0.5652 |
164
+ | 0.0041 | 98.1818 | 1080 | 0.0057 | 0.7816 | 0.8575 | 0.9984 | 0.7157 | 0.5648 |
165
+ | 0.0034 | 99.0909 | 1090 | 0.0057 | 0.7815 | 0.8566 | 0.9984 | 0.7139 | 0.5646 |
166
+ | 0.0016 | 100.0 | 1100 | 0.0057 | 0.7819 | 0.8573 | 0.9984 | 0.7153 | 0.5654 |
167
+
168
+
169
+ ### Framework versions
170
+
171
+ - Transformers 4.51.3
172
+ - Pytorch 2.6.0+cu124
173
+ - Datasets 3.6.0
174
+ - Tokenizers 0.21.1
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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