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segformer-b4-ade-finetuned-coastTrain

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  1. README.md +134 -198
  2. config.json +88 -0
  3. model.safetensors +3 -0
  4. runs/Mar06_13-38-07_52e3f927b2ef/events.out.tfevents.1709732291.52e3f927b2ef.26.0 +3 -0
  5. training_args.bin +3 -0
README.md CHANGED
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  ---
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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
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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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- ## How to Get Started with the Model
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- ## Training Details
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- ### Training Procedure
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- #### Preprocessing [optional]
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- ## Evaluation
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- #### Summary
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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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  ---
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+ license: other
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+ base_model: nvidia/segformer-b4-finetuned-ade-512-512
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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-b4-ade-finetuned-coastTrain
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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-b4-ade-finetuned-coastTrain
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+ This model is a fine-tuned version of [nvidia/segformer-b4-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b4-finetuned-ade-512-512) on the peldrak/coastTrain dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2784
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+ - Mean Iou: 0.7615
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+ - Mean Accuracy: 0.8569
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+ - Overall Accuracy: 0.9286
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+ - Accuracy Water: 0.9717
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+ - Accuracy Whitewater: 0.5408
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+ - Accuracy Sediment: 0.9245
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+ - Accuracy Other Natural Terrain: 0.8160
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+ - Accuracy Vegetation: 0.8979
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+ - Accuracy Development: 0.9242
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+ - Accuracy Unknown: 0.9232
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+ - Iou Water: 0.9253
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+ - Iou Whitewater: 0.4607
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+ - Iou Sediment: 0.8453
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+ - Iou Other Natural Terrain: 0.5582
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+ - Iou Vegetation: 0.8460
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+ - Iou Development: 0.8152
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+ - Iou Unknown: 0.8799
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+ - F1 Score: 0.9283
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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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+ 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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+ The following hyperparameters were used during training:
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+ - learning_rate: 6e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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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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+ - num_epochs: 20
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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 Water | Accuracy Whitewater | Accuracy Sediment | Accuracy Other Natural Terrain | Accuracy Vegetation | Accuracy Development | Accuracy Unknown | Iou Water | Iou Whitewater | Iou Sediment | Iou Other Natural Terrain | Iou Vegetation | Iou Development | Iou Unknown | F1 Score |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:|:--------:|
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+ | 1.663 | 0.16 | 20 | 1.5090 | 0.4228 | 0.5280 | 0.7404 | 0.7454 | 0.1290 | 0.6594 | 0.0007 | 0.9158 | 0.4431 | 0.8027 | 0.7085 | 0.0661 | 0.5003 | 0.0006 | 0.5240 | 0.3651 | 0.7953 | 0.7435 |
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+ | 1.4278 | 0.31 | 40 | 1.1477 | 0.4735 | 0.5606 | 0.8162 | 0.9166 | 0.0018 | 0.7142 | 0.0000 | 0.8720 | 0.5680 | 0.8513 | 0.8291 | 0.0018 | 0.5805 | 0.0000 | 0.6259 | 0.4328 | 0.8441 | 0.8075 |
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+ | 1.5096 | 0.47 | 60 | 0.8839 | 0.4469 | 0.5277 | 0.7917 | 0.8650 | 0.0000 | 0.5808 | 0.0 | 0.9654 | 0.4206 | 0.8618 | 0.8144 | 0.0000 | 0.5120 | 0.0 | 0.5659 | 0.3769 | 0.8589 | 0.7834 |
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+ | 1.2289 | 0.62 | 80 | 0.7240 | 0.4889 | 0.5772 | 0.8285 | 0.9213 | 0.0010 | 0.7241 | 0.0 | 0.8451 | 0.6604 | 0.8885 | 0.8586 | 0.0010 | 0.6189 | 0.0 | 0.6392 | 0.4241 | 0.8807 | 0.8226 |
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+ | 0.7603 | 0.78 | 100 | 0.5905 | 0.5238 | 0.6004 | 0.8575 | 0.9406 | 0.0008 | 0.7086 | 0.0 | 0.9081 | 0.7470 | 0.8978 | 0.8633 | 0.0008 | 0.6391 | 0.0 | 0.6927 | 0.5817 | 0.8892 | 0.8479 |
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+ | 1.1379 | 0.93 | 120 | 0.5625 | 0.5427 | 0.6168 | 0.8713 | 0.9660 | 0.0028 | 0.6806 | 0.0 | 0.8917 | 0.8819 | 0.8947 | 0.8537 | 0.0028 | 0.6139 | 0.0 | 0.7520 | 0.6952 | 0.8813 | 0.8597 |
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+ | 0.7824 | 1.09 | 140 | 0.5163 | 0.5320 | 0.6100 | 0.8653 | 0.9728 | 0.0001 | 0.6368 | 0.0 | 0.8720 | 0.8917 | 0.8963 | 0.8734 | 0.0001 | 0.6084 | 0.0 | 0.7073 | 0.6440 | 0.8911 | 0.8541 |
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+ | 0.6537 | 1.24 | 160 | 0.4595 | 0.5526 | 0.6278 | 0.8782 | 0.9497 | 0.0001 | 0.7542 | 0.0 | 0.9018 | 0.8883 | 0.9004 | 0.8791 | 0.0001 | 0.6505 | 0.0 | 0.7401 | 0.7045 | 0.8938 | 0.8682 |
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+ | 0.7204 | 1.4 | 180 | 0.4031 | 0.5593 | 0.6379 | 0.8834 | 0.9572 | 0.0004 | 0.8028 | 0.0 | 0.8648 | 0.9356 | 0.9048 | 0.8913 | 0.0004 | 0.7384 | 0.0 | 0.7428 | 0.6471 | 0.8950 | 0.8746 |
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+ | 0.663 | 1.55 | 200 | 0.4097 | 0.5592 | 0.6383 | 0.8813 | 0.9777 | 0.0 | 0.8640 | 0.0 | 0.8036 | 0.9289 | 0.8937 | 0.8718 | 0.0 | 0.7381 | 0.0 | 0.7349 | 0.6829 | 0.8869 | 0.8710 |
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+ | 0.4566 | 1.71 | 220 | 0.3912 | 0.5598 | 0.6405 | 0.8813 | 0.9515 | 0.0011 | 0.8262 | 0.0 | 0.8494 | 0.9577 | 0.8973 | 0.8743 | 0.0011 | 0.7426 | 0.0 | 0.7405 | 0.6706 | 0.8895 | 0.8722 |
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+ | 1.4951 | 1.86 | 240 | 0.3756 | 0.5566 | 0.6419 | 0.8804 | 0.9674 | 0.0006 | 0.8683 | 0.0 | 0.7963 | 0.9635 | 0.8971 | 0.8869 | 0.0006 | 0.7625 | 0.0 | 0.7352 | 0.6198 | 0.8909 | 0.8721 |
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+ | 0.6232 | 2.02 | 260 | 0.3842 | 0.5650 | 0.6357 | 0.8869 | 0.9528 | 0.0014 | 0.7446 | 0.0 | 0.9224 | 0.9236 | 0.9049 | 0.8897 | 0.0014 | 0.6955 | 0.0 | 0.7485 | 0.7258 | 0.8940 | 0.8767 |
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+ | 1.0104 | 2.17 | 280 | 0.3335 | 0.5791 | 0.6489 | 0.8974 | 0.9719 | 0.0028 | 0.8548 | 0.0 | 0.8796 | 0.9284 | 0.9049 | 0.8934 | 0.0028 | 0.7673 | 0.0 | 0.7825 | 0.7165 | 0.8912 | 0.8872 |
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+ | 0.4107 | 2.33 | 300 | 0.3663 | 0.5642 | 0.6456 | 0.8855 | 0.9677 | 0.0023 | 0.8966 | 0.0 | 0.8048 | 0.9413 | 0.9065 | 0.8765 | 0.0023 | 0.7372 | 0.0 | 0.7532 | 0.6814 | 0.8985 | 0.8758 |
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+ | 0.3112 | 2.48 | 320 | 0.3318 | 0.5833 | 0.6553 | 0.9006 | 0.9668 | 0.0132 | 0.8693 | 0.0 | 0.8855 | 0.9475 | 0.9047 | 0.9026 | 0.0132 | 0.7776 | 0.0 | 0.7936 | 0.6974 | 0.8991 | 0.8912 |
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+ | 0.6679 | 2.64 | 340 | 0.3357 | 0.5840 | 0.6520 | 0.8979 | 0.9620 | 0.0109 | 0.8876 | 0.0008 | 0.8768 | 0.9071 | 0.9187 | 0.8819 | 0.0109 | 0.7585 | 0.0008 | 0.8002 | 0.7742 | 0.8611 | 0.8873 |
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+ | 0.6522 | 2.79 | 360 | 0.3201 | 0.5850 | 0.6559 | 0.9015 | 0.9703 | 0.0209 | 0.8589 | 0.0010 | 0.8874 | 0.9440 | 0.9088 | 0.9037 | 0.0208 | 0.7665 | 0.0010 | 0.8052 | 0.7186 | 0.8794 | 0.8917 |
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+ | 0.569 | 2.95 | 380 | 0.3227 | 0.5899 | 0.6592 | 0.9000 | 0.9738 | 0.0292 | 0.8709 | 0.0294 | 0.8731 | 0.9292 | 0.9086 | 0.8907 | 0.0291 | 0.7557 | 0.0294 | 0.8057 | 0.7445 | 0.8744 | 0.8902 |
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+ | 0.5766 | 3.1 | 400 | 0.3537 | 0.5747 | 0.6401 | 0.8907 | 0.9663 | 0.0301 | 0.7411 | 0.0043 | 0.9260 | 0.9117 | 0.9013 | 0.8742 | 0.0300 | 0.7095 | 0.0043 | 0.7776 | 0.7304 | 0.8966 | 0.8804 |
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+ | 1.1582 | 3.26 | 420 | 0.3125 | 0.6175 | 0.6767 | 0.9030 | 0.9783 | 0.0329 | 0.8699 | 0.1886 | 0.9039 | 0.8574 | 0.9059 | 0.8795 | 0.0325 | 0.7877 | 0.1879 | 0.8158 | 0.7437 | 0.8757 | 0.8948 |
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+ | 0.4788 | 3.41 | 440 | 0.2963 | 0.6457 | 0.7017 | 0.9145 | 0.9791 | 0.0397 | 0.9018 | 0.2594 | 0.9025 | 0.9140 | 0.9151 | 0.8995 | 0.0394 | 0.8221 | 0.2566 | 0.8277 | 0.8050 | 0.8697 | 0.9069 |
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+ | 0.2278 | 3.57 | 460 | 0.3154 | 0.6225 | 0.6920 | 0.9049 | 0.9683 | 0.1006 | 0.8834 | 0.1576 | 0.8780 | 0.9448 | 0.9116 | 0.9053 | 0.0996 | 0.7952 | 0.1573 | 0.8066 | 0.7248 | 0.8684 | 0.8983 |
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+ | 0.4206 | 3.72 | 480 | 0.3235 | 0.5959 | 0.6553 | 0.9007 | 0.9666 | 0.0412 | 0.8435 | 0.0371 | 0.9305 | 0.8624 | 0.9060 | 0.9002 | 0.0411 | 0.7604 | 0.0371 | 0.7808 | 0.7811 | 0.8709 | 0.8912 |
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+ | 0.3314 | 3.88 | 500 | 0.3323 | 0.6125 | 0.6802 | 0.9019 | 0.9602 | 0.1699 | 0.8257 | 0.0432 | 0.9168 | 0.9415 | 0.9039 | 0.9100 | 0.1663 | 0.7722 | 0.0432 | 0.7877 | 0.7446 | 0.8636 | 0.8949 |
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+ | 0.8233 | 4.03 | 520 | 0.3092 | 0.6410 | 0.7039 | 0.9085 | 0.9714 | 0.1106 | 0.8881 | 0.2342 | 0.8985 | 0.9205 | 0.9042 | 0.9026 | 0.1083 | 0.7966 | 0.2326 | 0.8114 | 0.7720 | 0.8636 | 0.9022 |
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+ | 0.3436 | 4.19 | 540 | 0.3070 | 0.6464 | 0.7064 | 0.9100 | 0.9816 | 0.1226 | 0.9199 | 0.2470 | 0.8824 | 0.8815 | 0.9098 | 0.8936 | 0.1205 | 0.7881 | 0.2408 | 0.8151 | 0.7747 | 0.8918 | 0.9039 |
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+ | 0.3504 | 4.34 | 560 | 0.3084 | 0.6827 | 0.7459 | 0.9151 | 0.9749 | 0.2036 | 0.9063 | 0.4067 | 0.8906 | 0.9284 | 0.9107 | 0.9071 | 0.1971 | 0.8147 | 0.3798 | 0.8225 | 0.7894 | 0.8686 | 0.9111 |
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+ | 0.3461 | 4.5 | 580 | 0.3091 | 0.7164 | 0.8006 | 0.9183 | 0.9687 | 0.3107 | 0.8787 | 0.6910 | 0.8991 | 0.9385 | 0.9177 | 0.9184 | 0.2873 | 0.8206 | 0.5133 | 0.8230 | 0.7781 | 0.8740 | 0.9166 |
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+ | 0.9608 | 4.65 | 600 | 0.2973 | 0.6896 | 0.7680 | 0.9130 | 0.9772 | 0.2477 | 0.8727 | 0.5473 | 0.8849 | 0.9410 | 0.9055 | 0.9072 | 0.2363 | 0.8040 | 0.4150 | 0.8202 | 0.7792 | 0.8653 | 0.9101 |
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+ | 0.2724 | 4.81 | 620 | 0.2947 | 0.7055 | 0.7860 | 0.9169 | 0.9732 | 0.3313 | 0.9060 | 0.5587 | 0.8825 | 0.9406 | 0.9096 | 0.9133 | 0.3037 | 0.8241 | 0.4199 | 0.8221 | 0.7875 | 0.8681 | 0.9149 |
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+ | 0.2541 | 4.96 | 640 | 0.2897 | 0.7142 | 0.7929 | 0.9184 | 0.9748 | 0.3398 | 0.8850 | 0.6015 | 0.8874 | 0.9459 | 0.9163 | 0.9156 | 0.3142 | 0.8310 | 0.4651 | 0.8228 | 0.7766 | 0.8740 | 0.9166 |
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+ | 1.337 | 5.12 | 660 | 0.2950 | 0.7033 | 0.7721 | 0.9169 | 0.9612 | 0.3796 | 0.9062 | 0.3990 | 0.8976 | 0.9436 | 0.9178 | 0.9159 | 0.3526 | 0.8199 | 0.3732 | 0.8226 | 0.7638 | 0.8749 | 0.9148 |
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+ | 0.3685 | 5.27 | 680 | 0.2714 | 0.7369 | 0.8200 | 0.9227 | 0.9741 | 0.3947 | 0.8746 | 0.7536 | 0.9208 | 0.9100 | 0.9123 | 0.9194 | 0.3485 | 0.8370 | 0.5641 | 0.8375 | 0.7705 | 0.8815 | 0.9216 |
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+ | 0.2901 | 5.43 | 700 | 0.2848 | 0.7373 | 0.8149 | 0.9230 | 0.9704 | 0.4050 | 0.9080 | 0.6911 | 0.9158 | 0.9008 | 0.9131 | 0.9206 | 0.3630 | 0.8271 | 0.5450 | 0.8393 | 0.7948 | 0.8710 | 0.9217 |
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+ | 0.4242 | 5.58 | 720 | 0.2831 | 0.7343 | 0.8311 | 0.9195 | 0.9793 | 0.4392 | 0.8635 | 0.7955 | 0.8824 | 0.9400 | 0.9181 | 0.9140 | 0.3800 | 0.8157 | 0.5413 | 0.8335 | 0.7804 | 0.8752 | 0.9187 |
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+ | 0.2186 | 5.74 | 740 | 0.2713 | 0.7400 | 0.8010 | 0.9250 | 0.9680 | 0.4074 | 0.9073 | 0.5905 | 0.9445 | 0.8784 | 0.9107 | 0.9197 | 0.3715 | 0.8330 | 0.5342 | 0.8387 | 0.8023 | 0.8810 | 0.9234 |
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+ | 0.3729 | 5.89 | 760 | 0.2846 | 0.7528 | 0.8290 | 0.9233 | 0.9714 | 0.4906 | 0.8906 | 0.7037 | 0.9157 | 0.9274 | 0.9039 | 0.9199 | 0.4197 | 0.8272 | 0.6058 | 0.8448 | 0.7878 | 0.8641 | 0.9225 |
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+ | 0.29 | 6.05 | 780 | 0.2979 | 0.7411 | 0.8274 | 0.9225 | 0.9746 | 0.4241 | 0.9098 | 0.7544 | 0.8959 | 0.9247 | 0.9085 | 0.9222 | 0.3765 | 0.8288 | 0.5630 | 0.8343 | 0.7957 | 0.8672 | 0.9215 |
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+ | 0.1211 | 6.2 | 800 | 0.2962 | 0.7553 | 0.8266 | 0.9254 | 0.9722 | 0.4567 | 0.9034 | 0.7216 | 0.9218 | 0.8951 | 0.9153 | 0.9200 | 0.4076 | 0.8367 | 0.6068 | 0.8400 | 0.8034 | 0.8727 | 0.9242 |
109
+ | 0.2875 | 6.36 | 820 | 0.3040 | 0.7576 | 0.8358 | 0.9249 | 0.9705 | 0.5281 | 0.8742 | 0.7028 | 0.9110 | 0.9454 | 0.9184 | 0.9234 | 0.4444 | 0.8312 | 0.5986 | 0.8388 | 0.7919 | 0.8746 | 0.9243 |
110
+ | 0.1761 | 6.51 | 840 | 0.2623 | 0.7577 | 0.8422 | 0.9288 | 0.9742 | 0.4691 | 0.9182 | 0.7958 | 0.9109 | 0.9022 | 0.9249 | 0.9227 | 0.4026 | 0.8441 | 0.5856 | 0.8486 | 0.8191 | 0.8815 | 0.9279 |
111
+ | 0.2962 | 6.67 | 860 | 0.2828 | 0.7498 | 0.8469 | 0.9231 | 0.9651 | 0.5313 | 0.8896 | 0.7981 | 0.9125 | 0.9153 | 0.9166 | 0.9213 | 0.4305 | 0.8172 | 0.5763 | 0.8463 | 0.7849 | 0.8724 | 0.9228 |
112
+ | 0.3504 | 6.82 | 880 | 0.2912 | 0.7437 | 0.8384 | 0.9219 | 0.9793 | 0.4609 | 0.8613 | 0.8330 | 0.9077 | 0.9174 | 0.9094 | 0.9122 | 0.3974 | 0.8246 | 0.5504 | 0.8378 | 0.8159 | 0.8678 | 0.9211 |
113
+ | 0.2496 | 6.98 | 900 | 0.2838 | 0.7476 | 0.8480 | 0.9239 | 0.9729 | 0.4970 | 0.8875 | 0.8454 | 0.9128 | 0.9111 | 0.9092 | 0.9211 | 0.4346 | 0.8324 | 0.5334 | 0.8433 | 0.8005 | 0.8678 | 0.9235 |
114
+ | 1.2185 | 7.13 | 920 | 0.3104 | 0.7466 | 0.8454 | 0.9201 | 0.9732 | 0.5344 | 0.8610 | 0.8031 | 0.8965 | 0.9391 | 0.9105 | 0.9215 | 0.4476 | 0.8078 | 0.5605 | 0.8257 | 0.7948 | 0.8687 | 0.9197 |
115
+ | 0.1779 | 7.29 | 940 | 0.3212 | 0.7591 | 0.8515 | 0.9252 | 0.9751 | 0.5615 | 0.8844 | 0.7893 | 0.8949 | 0.9314 | 0.9236 | 0.9219 | 0.4660 | 0.8250 | 0.5769 | 0.8376 | 0.8097 | 0.8769 | 0.9247 |
116
+ | 0.4705 | 7.44 | 960 | 0.2663 | 0.7504 | 0.8429 | 0.9243 | 0.9776 | 0.4934 | 0.8899 | 0.8011 | 0.8994 | 0.9268 | 0.9118 | 0.9159 | 0.4335 | 0.8278 | 0.5520 | 0.8401 | 0.7987 | 0.8846 | 0.9237 |
117
+ | 0.2637 | 7.6 | 980 | 0.2561 | 0.7639 | 0.8449 | 0.9289 | 0.9717 | 0.5314 | 0.8805 | 0.7593 | 0.9309 | 0.9236 | 0.9172 | 0.9271 | 0.4443 | 0.8284 | 0.6024 | 0.8461 | 0.8074 | 0.8916 | 0.9284 |
118
+ | 0.1961 | 7.75 | 1000 | 0.2712 | 0.7486 | 0.8598 | 0.9250 | 0.9780 | 0.5721 | 0.8804 | 0.8483 | 0.8995 | 0.9282 | 0.9119 | 0.9184 | 0.4402 | 0.8297 | 0.5164 | 0.8466 | 0.8043 | 0.8848 | 0.9252 |
119
+ | 1.0785 | 7.91 | 1020 | 0.2494 | 0.7586 | 0.8472 | 0.9308 | 0.9752 | 0.5247 | 0.9059 | 0.7655 | 0.9142 | 0.9169 | 0.9280 | 0.9263 | 0.4457 | 0.8448 | 0.5380 | 0.8490 | 0.7996 | 0.9071 | 0.9304 |
120
+ | 0.1453 | 8.06 | 1040 | 0.2792 | 0.7454 | 0.8519 | 0.9254 | 0.9704 | 0.5225 | 0.8913 | 0.8028 | 0.8936 | 0.9675 | 0.9153 | 0.9283 | 0.4321 | 0.8367 | 0.5213 | 0.8397 | 0.7497 | 0.9099 | 0.9261 |
121
+ | 0.2332 | 8.22 | 1060 | 0.2774 | 0.7452 | 0.8434 | 0.9242 | 0.9771 | 0.5126 | 0.9145 | 0.7857 | 0.8916 | 0.9063 | 0.9157 | 0.9221 | 0.4303 | 0.8100 | 0.5271 | 0.8311 | 0.7858 | 0.9097 | 0.9240 |
122
+ | 0.1902 | 8.37 | 1080 | 0.2779 | 0.7382 | 0.8500 | 0.9228 | 0.9830 | 0.5081 | 0.8918 | 0.8470 | 0.8739 | 0.9259 | 0.9205 | 0.9178 | 0.4154 | 0.8383 | 0.4896 | 0.8345 | 0.7778 | 0.8937 | 0.9230 |
123
+ | 0.2892 | 8.53 | 1100 | 0.2735 | 0.7535 | 0.8527 | 0.9275 | 0.9726 | 0.5405 | 0.8697 | 0.8180 | 0.9147 | 0.9236 | 0.9300 | 0.9293 | 0.4539 | 0.8253 | 0.5226 | 0.8400 | 0.8145 | 0.8886 | 0.9273 |
124
+ | 0.2251 | 8.68 | 1120 | 0.2626 | 0.7627 | 0.8536 | 0.9295 | 0.9781 | 0.5566 | 0.9106 | 0.7753 | 0.8922 | 0.9349 | 0.9273 | 0.9239 | 0.4522 | 0.8465 | 0.5699 | 0.8455 | 0.8062 | 0.8949 | 0.9291 |
125
+ | 0.1345 | 8.84 | 1140 | 0.2558 | 0.7676 | 0.8542 | 0.9296 | 0.9784 | 0.5649 | 0.9180 | 0.7797 | 0.9048 | 0.9185 | 0.9151 | 0.9205 | 0.4585 | 0.8451 | 0.5930 | 0.8489 | 0.8174 | 0.8900 | 0.9292 |
126
+ | 0.18 | 8.99 | 1160 | 0.2737 | 0.7628 | 0.8566 | 0.9288 | 0.9781 | 0.5295 | 0.9059 | 0.8418 | 0.9066 | 0.9190 | 0.9150 | 0.9207 | 0.4404 | 0.8533 | 0.5818 | 0.8542 | 0.8169 | 0.8726 | 0.9283 |
127
+ | 0.282 | 9.15 | 1180 | 0.2705 | 0.7734 | 0.8487 | 0.9310 | 0.9717 | 0.5354 | 0.9220 | 0.7588 | 0.9230 | 0.9135 | 0.9165 | 0.9265 | 0.4527 | 0.8522 | 0.6342 | 0.8563 | 0.8178 | 0.8736 | 0.9304 |
128
+ | 0.1688 | 9.3 | 1200 | 0.2770 | 0.7695 | 0.8663 | 0.9317 | 0.9705 | 0.5896 | 0.9098 | 0.8282 | 0.9125 | 0.9241 | 0.9295 | 0.9315 | 0.4659 | 0.8490 | 0.5848 | 0.8541 | 0.8173 | 0.8840 | 0.9316 |
129
+ | 0.1043 | 9.46 | 1220 | 0.2784 | 0.7615 | 0.8569 | 0.9286 | 0.9717 | 0.5408 | 0.9245 | 0.8160 | 0.8979 | 0.9242 | 0.9232 | 0.9253 | 0.4607 | 0.8453 | 0.5582 | 0.8460 | 0.8152 | 0.8799 | 0.9283 |
130
+
131
+
132
+ ### Framework versions
133
+
134
+ - Transformers 4.38.1
135
+ - Pytorch 2.1.2
136
+ - Datasets 2.18.0
137
+ - Tokenizers 0.15.2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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