File size: 60,821 Bytes
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library_name: transformers
license: other
base_model: nvidia/segformer-b3-finetuned-cityscapes-1024-1024
tags:
- generated_from_trainer
model-index:
- name: SegFormer_b3_mappillary_
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SegFormer_b3_mappillary_
This model is a fine-tuned version of [nvidia/segformer-b3-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b3-finetuned-cityscapes-1024-1024) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9465
- Mean Iou: 0.6876
- Mean Accuracy: 0.8034
- Overall Accuracy: 0.9418
- Accuracy Construction--barrier--fence: 0.6800
- Accuracy Construction--barrier--guard-rail: 0.8164
- Accuracy Construction--barrier--other-barrier: 0.6949
- Accuracy Construction--barrier--wall: 0.6949
- Accuracy Construction--flat--road: 0.9600
- Accuracy Construction--flat--service-lane: 0.6083
- Accuracy Construction--flat--sidewalk: 0.8963
- Accuracy Construction--structure--building: 0.9474
- Accuracy Human--person: 0.8455
- Accuracy Human--rider--bicyclist: 0.7457
- Accuracy Marking--crosswalk-zebra: 0.8079
- Accuracy Marking--general: 0.7057
- Accuracy Nature--sky: 0.9903
- Accuracy Nature--terrain: 0.8351
- Accuracy Nature--vegetation: 0.9468
- Accuracy Object--support--pole: 0.5784
- Accuracy Object--support--traffic-sign-frame: 0.6875
- Accuracy Object--traffic-light: 0.7517
- Accuracy Object--traffic-sign--front: 0.8324
- Accuracy Object--vehicle--bicycle: 0.7600
- Accuracy Object--vehicle--bus: 0.8898
- Accuracy Object--vehicle--car: 0.9598
- Accuracy Object--vehicle--truck: 0.8427
- Iou Construction--barrier--fence: 0.5668
- Iou Construction--barrier--guard-rail: 0.6378
- Iou Construction--barrier--other-barrier: 0.5595
- Iou Construction--barrier--wall: 0.5403
- Iou Construction--flat--road: 0.9205
- Iou Construction--flat--service-lane: 0.4618
- Iou Construction--flat--sidewalk: 0.7968
- Iou Construction--structure--building: 0.8891
- Iou Human--person: 0.6797
- Iou Human--rider--bicyclist: 0.5718
- Iou Marking--crosswalk-zebra: 0.7125
- Iou Marking--general: 0.5860
- Iou Nature--sky: 0.9811
- Iou Nature--terrain: 0.6872
- Iou Nature--vegetation: 0.8949
- Iou Object--support--pole: 0.4599
- Iou Object--support--traffic-sign-frame: 0.5531
- Iou Object--traffic-light: 0.5835
- Iou Object--traffic-sign--front: 0.7231
- Iou Object--vehicle--bicycle: 0.5797
- Iou Object--vehicle--bus: 0.7946
- Iou Object--vehicle--car: 0.9055
- Iou Object--vehicle--truck: 0.7304
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Construction--barrier--fence | Accuracy Construction--barrier--guard-rail | Accuracy Construction--barrier--other-barrier | Accuracy Construction--barrier--wall | Accuracy Construction--flat--road | Accuracy Construction--flat--service-lane | Accuracy Construction--flat--sidewalk | Accuracy Construction--structure--building | Accuracy Human--person | Accuracy Human--rider--bicyclist | Accuracy Marking--crosswalk-zebra | Accuracy Marking--general | Accuracy Nature--sky | Accuracy Nature--terrain | Accuracy Nature--vegetation | Accuracy Object--support--pole | Accuracy Object--support--traffic-sign-frame | Accuracy Object--traffic-light | Accuracy Object--traffic-sign--front | Accuracy Object--vehicle--bicycle | Accuracy Object--vehicle--bus | Accuracy Object--vehicle--car | Accuracy Object--vehicle--truck | Iou Construction--barrier--fence | Iou Construction--barrier--guard-rail | Iou Construction--barrier--other-barrier | Iou Construction--barrier--wall | Iou Construction--flat--road | Iou Construction--flat--service-lane | Iou Construction--flat--sidewalk | Iou Construction--structure--building | Iou Human--person | Iou Human--rider--bicyclist | Iou Marking--crosswalk-zebra | Iou Marking--general | Iou Nature--sky | Iou Nature--terrain | Iou Nature--vegetation | Iou Object--support--pole | Iou Object--support--traffic-sign-frame | Iou Object--traffic-light | Iou Object--traffic-sign--front | Iou Object--vehicle--bicycle | Iou Object--vehicle--bus | Iou Object--vehicle--car | Iou Object--vehicle--truck |
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| 1.3425 | 0.4444 | 1000 | 1.2765 | 0.2545 | 0.3044 | 0.8800 | 0.6134 | 0.0 | 0.0033 | 0.0266 | 0.9695 | 0.0 | 0.7990 | 0.9191 | 0.0001 | 0.0 | 0.0000 | 0.0467 | 0.9827 | 0.6667 | 0.9530 | 0.0001 | 0.0 | 0.0 | 0.0845 | 0.0 | 0.0000 | 0.9361 | 0.0 | 0.3190 | 0.0 | 0.0033 | 0.0263 | 0.8350 | 0.0 | 0.6117 | 0.8074 | 0.0001 | 0.0 | 0.0000 | 0.0426 | 0.9577 | 0.5511 | 0.8295 | 0.0001 | 0.0 | 0.0 | 0.0842 | 0.0 | 0.0000 | 0.7854 | 0.0 |
| 1.1254 | 0.8889 | 2000 | 1.0846 | 0.4898 | 0.5869 | 0.9169 | 0.6043 | 0.7972 | 0.5664 | 0.5400 | 0.9357 | 0.0 | 0.8852 | 0.9351 | 0.6966 | 0.0 | 0.6074 | 0.5823 | 0.9887 | 0.8180 | 0.9342 | 0.4084 | 0.0 | 0.0577 | 0.7376 | 0.0 | 0.7325 | 0.9520 | 0.7184 | 0.4699 | 0.4923 | 0.4243 | 0.4304 | 0.8822 | 0.0 | 0.6986 | 0.8531 | 0.5266 | 0.0 | 0.5141 | 0.4423 | 0.9723 | 0.6271 | 0.8680 | 0.3223 | 0.0 | 0.0574 | 0.5713 | 0.0 | 0.6531 | 0.8591 | 0.6014 |
| 1.0545 | 1.3333 | 3000 | 1.0331 | 0.5437 | 0.6380 | 0.9253 | 0.6281 | 0.6042 | 0.6614 | 0.6689 | 0.9598 | 0.1969 | 0.8240 | 0.9329 | 0.8189 | 0.0 | 0.6535 | 0.5900 | 0.9890 | 0.7623 | 0.9431 | 0.4452 | 0.0576 | 0.6242 | 0.7553 | 0.0473 | 0.8173 | 0.9560 | 0.7380 | 0.4962 | 0.5220 | 0.4592 | 0.5118 | 0.8942 | 0.1780 | 0.7109 | 0.8662 | 0.5534 | 0.0 | 0.5445 | 0.4827 | 0.9761 | 0.6458 | 0.8780 | 0.3593 | 0.0575 | 0.4728 | 0.6404 | 0.0470 | 0.7192 | 0.8673 | 0.6224 |
| 1.0702 | 1.7778 | 4000 | 1.0152 | 0.5925 | 0.7119 | 0.9278 | 0.6339 | 0.8483 | 0.7095 | 0.6748 | 0.9643 | 0.4595 | 0.8005 | 0.9253 | 0.8096 | 0.0 | 0.6926 | 0.5453 | 0.9910 | 0.7992 | 0.9379 | 0.5329 | 0.4324 | 0.6756 | 0.7847 | 0.5913 | 0.8819 | 0.9506 | 0.7321 | 0.4993 | 0.5053 | 0.4767 | 0.4969 | 0.8986 | 0.3391 | 0.7166 | 0.8689 | 0.5999 | 0.0 | 0.6029 | 0.4807 | 0.9773 | 0.6585 | 0.8804 | 0.3939 | 0.3874 | 0.5017 | 0.6616 | 0.4461 | 0.7138 | 0.8789 | 0.6421 |
| 0.9517 | 2.2222 | 5000 | 0.9929 | 0.6200 | 0.7373 | 0.9309 | 0.6915 | 0.7243 | 0.6735 | 0.6030 | 0.9513 | 0.5424 | 0.8577 | 0.9424 | 0.8352 | 0.1893 | 0.7769 | 0.6458 | 0.9894 | 0.8427 | 0.9335 | 0.5217 | 0.5057 | 0.6906 | 0.8021 | 0.7058 | 0.8375 | 0.9478 | 0.7480 | 0.5345 | 0.5905 | 0.5076 | 0.4934 | 0.9028 | 0.4129 | 0.7423 | 0.8723 | 0.6025 | 0.1827 | 0.6044 | 0.5226 | 0.9785 | 0.6709 | 0.8819 | 0.4060 | 0.4432 | 0.5208 | 0.6637 | 0.4977 | 0.7046 | 0.8846 | 0.6402 |
| 1.0168 | 2.6667 | 6000 | 0.9903 | 0.6447 | 0.7756 | 0.9316 | 0.6411 | 0.8493 | 0.7208 | 0.6234 | 0.9524 | 0.8120 | 0.8049 | 0.9446 | 0.8134 | 0.6314 | 0.7481 | 0.6312 | 0.9903 | 0.8097 | 0.9423 | 0.4974 | 0.6191 | 0.6996 | 0.7810 | 0.7204 | 0.8662 | 0.9570 | 0.7842 | 0.5213 | 0.5644 | 0.5073 | 0.5028 | 0.8994 | 0.4049 | 0.7222 | 0.8763 | 0.6491 | 0.5067 | 0.6396 | 0.5282 | 0.9784 | 0.6763 | 0.8856 | 0.4040 | 0.4873 | 0.5286 | 0.6815 | 0.5415 | 0.7612 | 0.8878 | 0.6727 |
| 1.0717 | 3.1111 | 7000 | 0.9785 | 0.6508 | 0.7757 | 0.9340 | 0.6746 | 0.7986 | 0.6721 | 0.6354 | 0.9472 | 0.6116 | 0.8959 | 0.9425 | 0.8280 | 0.6989 | 0.7759 | 0.6393 | 0.9887 | 0.8304 | 0.9454 | 0.5240 | 0.5967 | 0.7134 | 0.7888 | 0.7306 | 0.8959 | 0.9525 | 0.7551 | 0.5325 | 0.6073 | 0.5107 | 0.5195 | 0.9065 | 0.4417 | 0.7717 | 0.8754 | 0.6435 | 0.5295 | 0.6266 | 0.5228 | 0.9791 | 0.6817 | 0.8861 | 0.4167 | 0.4969 | 0.5338 | 0.6829 | 0.5395 | 0.7210 | 0.8904 | 0.6522 |
| 1.2969 | 3.5556 | 8000 | 0.9726 | 0.6607 | 0.7788 | 0.9353 | 0.6778 | 0.7819 | 0.6816 | 0.6455 | 0.9504 | 0.7169 | 0.8924 | 0.9420 | 0.8285 | 0.7140 | 0.7469 | 0.6249 | 0.9890 | 0.8405 | 0.9465 | 0.5522 | 0.6019 | 0.6983 | 0.8032 | 0.6940 | 0.8498 | 0.9571 | 0.7779 | 0.5486 | 0.6167 | 0.5305 | 0.5219 | 0.9080 | 0.4372 | 0.7636 | 0.8778 | 0.6530 | 0.5394 | 0.6504 | 0.5336 | 0.9795 | 0.6808 | 0.8879 | 0.4286 | 0.5104 | 0.5458 | 0.6881 | 0.5569 | 0.7712 | 0.8914 | 0.6741 |
| 1.066 | 4.0 | 9000 | 0.9686 | 0.6606 | 0.7836 | 0.9352 | 0.6679 | 0.8057 | 0.6835 | 0.6398 | 0.9465 | 0.6595 | 0.9040 | 0.9473 | 0.8201 | 0.7057 | 0.7608 | 0.6587 | 0.9889 | 0.8467 | 0.9406 | 0.5365 | 0.6196 | 0.6992 | 0.7951 | 0.7535 | 0.8633 | 0.9606 | 0.8193 | 0.5390 | 0.6280 | 0.5393 | 0.5135 | 0.9071 | 0.4355 | 0.7590 | 0.8777 | 0.6543 | 0.5289 | 0.6610 | 0.5504 | 0.9798 | 0.6819 | 0.8884 | 0.4267 | 0.5145 | 0.5534 | 0.6897 | 0.5437 | 0.7678 | 0.8903 | 0.6651 |
| 0.9934 | 4.4444 | 10000 | 0.9634 | 0.6599 | 0.7918 | 0.9362 | 0.6866 | 0.8041 | 0.6551 | 0.7025 | 0.9550 | 0.7200 | 0.8742 | 0.9367 | 0.8221 | 0.6826 | 0.7694 | 0.6683 | 0.9876 | 0.8475 | 0.9470 | 0.5354 | 0.6706 | 0.7083 | 0.8183 | 0.8001 | 0.8479 | 0.9553 | 0.8165 | 0.5422 | 0.6144 | 0.5272 | 0.5433 | 0.9112 | 0.4443 | 0.7763 | 0.8806 | 0.6577 | 0.4982 | 0.6644 | 0.5512 | 0.9794 | 0.6814 | 0.8875 | 0.4287 | 0.5303 | 0.5516 | 0.6906 | 0.4916 | 0.7697 | 0.8931 | 0.6635 |
| 0.9396 | 4.8889 | 11000 | 0.9608 | 0.6706 | 0.7865 | 0.9375 | 0.6659 | 0.7949 | 0.6840 | 0.6375 | 0.9573 | 0.6485 | 0.8755 | 0.9484 | 0.8153 | 0.7668 | 0.7889 | 0.6647 | 0.9907 | 0.8336 | 0.9425 | 0.5406 | 0.6331 | 0.7248 | 0.8096 | 0.7237 | 0.8555 | 0.9560 | 0.8317 | 0.5476 | 0.6242 | 0.5600 | 0.5252 | 0.9132 | 0.4695 | 0.7721 | 0.8799 | 0.6674 | 0.5666 | 0.6676 | 0.5550 | 0.9800 | 0.6793 | 0.8900 | 0.4318 | 0.5307 | 0.5538 | 0.7017 | 0.5603 | 0.7705 | 0.8948 | 0.6825 |
| 1.0232 | 5.3333 | 12000 | 0.9602 | 0.6665 | 0.7811 | 0.9374 | 0.6948 | 0.7343 | 0.6613 | 0.6420 | 0.9553 | 0.5946 | 0.8991 | 0.9486 | 0.8225 | 0.6960 | 0.7909 | 0.6412 | 0.9903 | 0.8492 | 0.9400 | 0.5514 | 0.6470 | 0.7395 | 0.8046 | 0.7763 | 0.8375 | 0.9602 | 0.7878 | 0.5510 | 0.6077 | 0.5071 | 0.5136 | 0.9148 | 0.4577 | 0.7800 | 0.8786 | 0.6628 | 0.5361 | 0.6571 | 0.5479 | 0.9802 | 0.6900 | 0.8905 | 0.4368 | 0.5341 | 0.5536 | 0.7050 | 0.5662 | 0.7751 | 0.8941 | 0.6901 |
| 1.003 | 5.7778 | 13000 | 0.9568 | 0.6665 | 0.7868 | 0.9377 | 0.6587 | 0.7905 | 0.7084 | 0.6866 | 0.9556 | 0.5506 | 0.9020 | 0.9400 | 0.8251 | 0.6929 | 0.7483 | 0.6743 | 0.9903 | 0.8455 | 0.9451 | 0.5720 | 0.6413 | 0.7217 | 0.8021 | 0.7812 | 0.9025 | 0.9549 | 0.8077 | 0.5459 | 0.6315 | 0.5463 | 0.5151 | 0.9130 | 0.4338 | 0.7792 | 0.8823 | 0.6636 | 0.5190 | 0.6567 | 0.5635 | 0.9804 | 0.6903 | 0.8912 | 0.4402 | 0.5333 | 0.5608 | 0.7045 | 0.5575 | 0.7314 | 0.8977 | 0.6934 |
| 1.0042 | 6.2222 | 14000 | 0.9524 | 0.6759 | 0.7923 | 0.9393 | 0.7133 | 0.7758 | 0.6648 | 0.6809 | 0.9609 | 0.5916 | 0.8752 | 0.9431 | 0.8173 | 0.7671 | 0.7821 | 0.6872 | 0.9899 | 0.8290 | 0.9472 | 0.5473 | 0.6624 | 0.7405 | 0.8113 | 0.7582 | 0.8978 | 0.9559 | 0.8234 | 0.5641 | 0.6347 | 0.5491 | 0.5441 | 0.9163 | 0.4613 | 0.7830 | 0.8823 | 0.6701 | 0.5536 | 0.6898 | 0.5640 | 0.9804 | 0.6937 | 0.8926 | 0.4372 | 0.5404 | 0.5589 | 0.7084 | 0.5520 | 0.7715 | 0.8982 | 0.7006 |
| 1.1329 | 6.6667 | 15000 | 0.9514 | 0.6774 | 0.7927 | 0.9398 | 0.6569 | 0.8159 | 0.6914 | 0.7315 | 0.9660 | 0.6223 | 0.8705 | 0.9411 | 0.8354 | 0.7894 | 0.7848 | 0.6641 | 0.9904 | 0.8079 | 0.9484 | 0.5592 | 0.6137 | 0.7370 | 0.8166 | 0.7257 | 0.8908 | 0.9567 | 0.8171 | 0.5473 | 0.6384 | 0.5535 | 0.5377 | 0.9185 | 0.4755 | 0.7851 | 0.8851 | 0.6648 | 0.5548 | 0.6978 | 0.5690 | 0.9804 | 0.6840 | 0.8920 | 0.4418 | 0.5268 | 0.5655 | 0.7083 | 0.5549 | 0.7825 | 0.9004 | 0.7167 |
| 0.9527 | 7.1111 | 16000 | 0.9583 | 0.6728 | 0.7944 | 0.9376 | 0.7232 | 0.8220 | 0.6712 | 0.6710 | 0.9460 | 0.5458 | 0.9175 | 0.9498 | 0.8358 | 0.7740 | 0.8209 | 0.6919 | 0.9886 | 0.7697 | 0.9432 | 0.5861 | 0.6600 | 0.7535 | 0.8073 | 0.7433 | 0.8595 | 0.9565 | 0.8342 | 0.5638 | 0.6379 | 0.5591 | 0.5278 | 0.9112 | 0.4177 | 0.7759 | 0.8823 | 0.6661 | 0.5778 | 0.6754 | 0.5674 | 0.9800 | 0.6777 | 0.8915 | 0.4479 | 0.5367 | 0.5604 | 0.7129 | 0.5705 | 0.7603 | 0.8962 | 0.6784 |
| 0.9354 | 7.5556 | 17000 | 0.9510 | 0.6782 | 0.7953 | 0.9394 | 0.7130 | 0.8167 | 0.6822 | 0.6615 | 0.9568 | 0.5684 | 0.8837 | 0.9473 | 0.8334 | 0.7628 | 0.8149 | 0.7023 | 0.9909 | 0.8648 | 0.9403 | 0.5304 | 0.7092 | 0.7378 | 0.8073 | 0.7204 | 0.8791 | 0.9603 | 0.8079 | 0.5636 | 0.6413 | 0.5599 | 0.5420 | 0.9149 | 0.4203 | 0.7894 | 0.8852 | 0.6728 | 0.5804 | 0.6862 | 0.5697 | 0.9806 | 0.6873 | 0.8920 | 0.4373 | 0.5428 | 0.5693 | 0.7165 | 0.5854 | 0.7695 | 0.8976 | 0.6953 |
| 0.908 | 8.0 | 18000 | 0.9500 | 0.6777 | 0.8005 | 0.9399 | 0.7093 | 0.7857 | 0.7006 | 0.6132 | 0.9574 | 0.6719 | 0.9108 | 0.9499 | 0.8358 | 0.8035 | 0.8172 | 0.6634 | 0.9909 | 0.8249 | 0.9405 | 0.5649 | 0.6957 | 0.7319 | 0.8177 | 0.7674 | 0.8684 | 0.9584 | 0.8323 | 0.5724 | 0.6381 | 0.5655 | 0.5112 | 0.9189 | 0.4591 | 0.8012 | 0.8812 | 0.6700 | 0.5631 | 0.6707 | 0.5631 | 0.9807 | 0.6972 | 0.8931 | 0.4485 | 0.5452 | 0.5741 | 0.7061 | 0.5525 | 0.7896 | 0.8983 | 0.6877 |
| 0.8843 | 8.4444 | 19000 | 0.9511 | 0.6796 | 0.7993 | 0.9401 | 0.7004 | 0.8178 | 0.6519 | 0.7174 | 0.9572 | 0.6224 | 0.9077 | 0.9474 | 0.8269 | 0.7723 | 0.7965 | 0.6727 | 0.9898 | 0.8237 | 0.9426 | 0.5698 | 0.6640 | 0.7518 | 0.8313 | 0.7532 | 0.8897 | 0.9604 | 0.8164 | 0.5690 | 0.6276 | 0.5562 | 0.5450 | 0.9174 | 0.4548 | 0.7880 | 0.8860 | 0.6724 | 0.5485 | 0.6921 | 0.5652 | 0.9808 | 0.6932 | 0.8931 | 0.4511 | 0.5478 | 0.5682 | 0.7127 | 0.5552 | 0.7931 | 0.9005 | 0.7126 |
| 0.9566 | 8.8889 | 20000 | 0.9472 | 0.6787 | 0.7985 | 0.9404 | 0.7049 | 0.7575 | 0.6818 | 0.6768 | 0.9555 | 0.6506 | 0.9006 | 0.9482 | 0.8200 | 0.7445 | 0.8169 | 0.7053 | 0.9907 | 0.8590 | 0.9422 | 0.5622 | 0.6687 | 0.7411 | 0.8172 | 0.7526 | 0.8892 | 0.9563 | 0.8225 | 0.5685 | 0.6161 | 0.5442 | 0.5352 | 0.9182 | 0.4805 | 0.7979 | 0.8858 | 0.6724 | 0.5217 | 0.7002 | 0.5769 | 0.9808 | 0.6892 | 0.8930 | 0.4473 | 0.5409 | 0.5736 | 0.7173 | 0.5570 | 0.7833 | 0.9012 | 0.7092 |
| 0.9415 | 9.3333 | 21000 | 0.9548 | 0.6777 | 0.7951 | 0.9399 | 0.6808 | 0.8055 | 0.6928 | 0.6894 | 0.9626 | 0.6218 | 0.8789 | 0.9473 | 0.8325 | 0.7719 | 0.7636 | 0.6931 | 0.9905 | 0.8210 | 0.9433 | 0.5703 | 0.6522 | 0.7388 | 0.8204 | 0.7483 | 0.8854 | 0.9558 | 0.8208 | 0.5609 | 0.6273 | 0.5627 | 0.5270 | 0.9183 | 0.4640 | 0.7927 | 0.8843 | 0.6738 | 0.5507 | 0.6784 | 0.5680 | 0.9808 | 0.6784 | 0.8926 | 0.4504 | 0.5386 | 0.5761 | 0.7179 | 0.5593 | 0.7798 | 0.9016 | 0.7029 |
| 0.9323 | 9.7778 | 22000 | 0.9473 | 0.6801 | 0.8004 | 0.9401 | 0.6976 | 0.7806 | 0.7003 | 0.6717 | 0.9568 | 0.5970 | 0.9007 | 0.9481 | 0.8302 | 0.7417 | 0.8203 | 0.6961 | 0.9901 | 0.8677 | 0.9394 | 0.5692 | 0.7008 | 0.7428 | 0.8164 | 0.7686 | 0.8670 | 0.9575 | 0.8475 | 0.5692 | 0.6195 | 0.5507 | 0.5221 | 0.9184 | 0.4674 | 0.7960 | 0.8852 | 0.6797 | 0.5621 | 0.6920 | 0.5766 | 0.9808 | 0.6814 | 0.8924 | 0.4532 | 0.5521 | 0.5755 | 0.7186 | 0.5649 | 0.7876 | 0.9007 | 0.6960 |
| 0.9317 | 10.2222 | 23000 | 0.9506 | 0.6815 | 0.8006 | 0.9404 | 0.7121 | 0.8045 | 0.7110 | 0.6755 | 0.9526 | 0.6830 | 0.9059 | 0.9489 | 0.8399 | 0.7169 | 0.8078 | 0.7014 | 0.9907 | 0.8323 | 0.9433 | 0.5671 | 0.6756 | 0.7389 | 0.8177 | 0.7487 | 0.8808 | 0.9624 | 0.7964 | 0.5753 | 0.6195 | 0.5531 | 0.5441 | 0.9163 | 0.4886 | 0.7851 | 0.8864 | 0.6739 | 0.5363 | 0.7008 | 0.5833 | 0.9809 | 0.6951 | 0.8940 | 0.4526 | 0.5529 | 0.5792 | 0.7162 | 0.5601 | 0.7802 | 0.9008 | 0.7007 |
| 1.0166 | 10.6667 | 24000 | 0.9497 | 0.6823 | 0.8009 | 0.9409 | 0.7061 | 0.8256 | 0.7003 | 0.6733 | 0.9620 | 0.6408 | 0.8786 | 0.9487 | 0.8402 | 0.7293 | 0.8262 | 0.6825 | 0.9900 | 0.8168 | 0.9442 | 0.5813 | 0.6780 | 0.7523 | 0.8205 | 0.7576 | 0.8812 | 0.9605 | 0.8254 | 0.5732 | 0.6280 | 0.5625 | 0.5400 | 0.9194 | 0.4772 | 0.7939 | 0.8869 | 0.6747 | 0.5375 | 0.6894 | 0.5738 | 0.9810 | 0.6867 | 0.8939 | 0.4566 | 0.5533 | 0.5755 | 0.7220 | 0.5683 | 0.7850 | 0.9019 | 0.7119 |
| 0.9552 | 11.1111 | 25000 | 0.9514 | 0.6839 | 0.7956 | 0.9410 | 0.6848 | 0.8055 | 0.6767 | 0.6773 | 0.9627 | 0.5812 | 0.8873 | 0.9497 | 0.8387 | 0.7154 | 0.7963 | 0.6822 | 0.9906 | 0.8297 | 0.9442 | 0.5835 | 0.7141 | 0.7414 | 0.8294 | 0.7613 | 0.8648 | 0.9590 | 0.8237 | 0.5684 | 0.6419 | 0.5621 | 0.5352 | 0.9190 | 0.4497 | 0.7908 | 0.8864 | 0.6778 | 0.5532 | 0.6995 | 0.5788 | 0.9809 | 0.6913 | 0.8940 | 0.4588 | 0.5643 | 0.5798 | 0.7216 | 0.5840 | 0.7834 | 0.9024 | 0.7064 |
| 0.9968 | 11.5556 | 26000 | 0.9473 | 0.6823 | 0.8022 | 0.9405 | 0.6980 | 0.8150 | 0.6930 | 0.6878 | 0.9563 | 0.6225 | 0.9037 | 0.9493 | 0.8403 | 0.7341 | 0.8076 | 0.6876 | 0.9899 | 0.8503 | 0.9409 | 0.5806 | 0.7029 | 0.7571 | 0.8294 | 0.7585 | 0.8656 | 0.9608 | 0.8181 | 0.5709 | 0.6372 | 0.5610 | 0.5313 | 0.9178 | 0.4644 | 0.7953 | 0.8871 | 0.6764 | 0.5590 | 0.6897 | 0.5793 | 0.9810 | 0.6847 | 0.8930 | 0.4576 | 0.5509 | 0.5765 | 0.7208 | 0.5659 | 0.7791 | 0.9003 | 0.7139 |
| 0.9555 | 12.0 | 27000 | 0.9485 | 0.6820 | 0.7971 | 0.9410 | 0.6763 | 0.8329 | 0.6877 | 0.6743 | 0.9597 | 0.5537 | 0.8978 | 0.9477 | 0.8351 | 0.7162 | 0.7749 | 0.7018 | 0.9903 | 0.8405 | 0.9465 | 0.5790 | 0.6895 | 0.7582 | 0.8251 | 0.7846 | 0.8723 | 0.9619 | 0.8265 | 0.5656 | 0.6363 | 0.5703 | 0.5333 | 0.9185 | 0.4344 | 0.7931 | 0.8880 | 0.6813 | 0.5455 | 0.6994 | 0.5813 | 0.9810 | 0.6898 | 0.8941 | 0.4572 | 0.5481 | 0.5793 | 0.7227 | 0.5605 | 0.7893 | 0.9011 | 0.7158 |
| 0.9325 | 12.4444 | 28000 | 0.9490 | 0.6829 | 0.8029 | 0.9412 | 0.6928 | 0.7981 | 0.7084 | 0.6846 | 0.9600 | 0.6005 | 0.8961 | 0.9461 | 0.8434 | 0.7252 | 0.8082 | 0.6989 | 0.9895 | 0.8267 | 0.9466 | 0.5831 | 0.7146 | 0.7602 | 0.8321 | 0.7699 | 0.8824 | 0.9595 | 0.8409 | 0.5665 | 0.6315 | 0.5542 | 0.5330 | 0.9202 | 0.4673 | 0.7991 | 0.8880 | 0.6772 | 0.5431 | 0.7031 | 0.5830 | 0.9808 | 0.6882 | 0.8944 | 0.4585 | 0.5421 | 0.5785 | 0.7214 | 0.5722 | 0.7859 | 0.9029 | 0.7148 |
| 0.9039 | 12.8889 | 29000 | 0.9449 | 0.6862 | 0.7980 | 0.9417 | 0.6883 | 0.8044 | 0.6908 | 0.6811 | 0.9607 | 0.5807 | 0.8982 | 0.9472 | 0.8457 | 0.7476 | 0.7988 | 0.7018 | 0.9902 | 0.8109 | 0.9498 | 0.5777 | 0.6796 | 0.7492 | 0.8243 | 0.7585 | 0.8824 | 0.9601 | 0.8252 | 0.5666 | 0.6462 | 0.5669 | 0.5394 | 0.9201 | 0.4516 | 0.7983 | 0.8889 | 0.6767 | 0.5723 | 0.7040 | 0.5827 | 0.9811 | 0.6910 | 0.8946 | 0.4583 | 0.5528 | 0.5807 | 0.7217 | 0.5810 | 0.7921 | 0.9035 | 0.7126 |
| 0.936 | 13.3333 | 30000 | 0.9458 | 0.6848 | 0.8025 | 0.9414 | 0.7056 | 0.7957 | 0.7069 | 0.6933 | 0.9611 | 0.6014 | 0.8949 | 0.9441 | 0.8329 | 0.7333 | 0.8139 | 0.6904 | 0.9892 | 0.8341 | 0.9471 | 0.5854 | 0.7129 | 0.7506 | 0.8271 | 0.7628 | 0.8837 | 0.9617 | 0.8291 | 0.5683 | 0.6342 | 0.5463 | 0.5447 | 0.9202 | 0.4538 | 0.7957 | 0.8889 | 0.6829 | 0.5634 | 0.7052 | 0.5821 | 0.9809 | 0.6864 | 0.8944 | 0.4613 | 0.5455 | 0.5809 | 0.7217 | 0.5705 | 0.7921 | 0.9039 | 0.7260 |
| 0.9636 | 13.7778 | 31000 | 0.9470 | 0.6856 | 0.8003 | 0.9416 | 0.6972 | 0.8154 | 0.6967 | 0.6890 | 0.9617 | 0.6171 | 0.8940 | 0.9500 | 0.8438 | 0.7202 | 0.7881 | 0.7000 | 0.9909 | 0.8198 | 0.9431 | 0.5814 | 0.6997 | 0.7480 | 0.8320 | 0.7590 | 0.8715 | 0.9590 | 0.8301 | 0.5733 | 0.6329 | 0.5570 | 0.5440 | 0.9204 | 0.4667 | 0.7962 | 0.8880 | 0.6784 | 0.5502 | 0.7071 | 0.5841 | 0.9811 | 0.6878 | 0.8946 | 0.4602 | 0.5583 | 0.5827 | 0.7208 | 0.5780 | 0.7890 | 0.9039 | 0.7138 |
| 0.8986 | 14.2222 | 32000 | 0.9472 | 0.6864 | 0.8013 | 0.9417 | 0.6857 | 0.7904 | 0.6909 | 0.6910 | 0.9619 | 0.6165 | 0.8941 | 0.9449 | 0.8406 | 0.7531 | 0.8035 | 0.7000 | 0.9899 | 0.8171 | 0.9487 | 0.5884 | 0.6967 | 0.7541 | 0.8281 | 0.7664 | 0.8738 | 0.9624 | 0.8329 | 0.5684 | 0.6394 | 0.5559 | 0.5415 | 0.9206 | 0.4706 | 0.7962 | 0.8889 | 0.6812 | 0.5703 | 0.7083 | 0.5841 | 0.9811 | 0.6856 | 0.8946 | 0.4616 | 0.5548 | 0.5814 | 0.7220 | 0.5702 | 0.7908 | 0.9032 | 0.7170 |
| 0.9031 | 14.6667 | 33000 | 0.9465 | 0.6876 | 0.8034 | 0.9418 | 0.6800 | 0.8164 | 0.6949 | 0.6949 | 0.9600 | 0.6083 | 0.8963 | 0.9474 | 0.8455 | 0.7457 | 0.8079 | 0.7057 | 0.9903 | 0.8351 | 0.9468 | 0.5784 | 0.6875 | 0.7517 | 0.8324 | 0.7600 | 0.8898 | 0.9598 | 0.8427 | 0.5668 | 0.6378 | 0.5595 | 0.5403 | 0.9205 | 0.4618 | 0.7968 | 0.8891 | 0.6797 | 0.5718 | 0.7125 | 0.5860 | 0.9811 | 0.6872 | 0.8949 | 0.4599 | 0.5531 | 0.5835 | 0.7231 | 0.5797 | 0.7946 | 0.9055 | 0.7304 |
### Framework versions
- Transformers 4.55.2
- Pytorch 2.7.1+cu118
- Datasets 4.0.0
- Tokenizers 0.21.4
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