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---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-example-pytorch-blog
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-b0-example-pytorch-blog
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2757
- Mean Iou: 0.1462
- Mean Accuracy: 0.2006
- Overall Accuracy: 0.7257
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.8968
- Accuracy Flat-sidewalk: 0.9232
- Accuracy Flat-crosswalk: 0.0
- Accuracy Flat-cyclinglane: 0.0013
- Accuracy Flat-parkingdriveway: 0.0024
- Accuracy Flat-railtrack: nan
- Accuracy Flat-curb: 0.0
- Accuracy Human-person: 0.0
- Accuracy Human-rider: 0.0
- Accuracy Vehicle-car: 0.8803
- Accuracy Vehicle-truck: 0.0
- Accuracy Vehicle-bus: 0.0
- Accuracy Vehicle-tramtrain: nan
- Accuracy Vehicle-motorcycle: 0.0
- Accuracy Vehicle-bicycle: 0.0
- Accuracy Vehicle-caravan: 0.0
- Accuracy Vehicle-cartrailer: 0.0
- Accuracy Construction-building: 0.8822
- Accuracy Construction-door: 0.0
- Accuracy Construction-wall: 0.0000
- Accuracy Construction-fenceguardrail: 0.0
- Accuracy Construction-bridge: 0.0
- Accuracy Construction-tunnel: nan
- Accuracy Construction-stairs: 0.0
- Accuracy Object-pole: 0.0
- Accuracy Object-trafficsign: 0.0
- Accuracy Object-trafficlight: 0.0
- Accuracy Nature-vegetation: 0.8802
- Accuracy Nature-terrain: 0.8441
- Accuracy Sky: 0.9068
- Accuracy Void-ground: 0.0
- Accuracy Void-dynamic: 0.0
- Accuracy Void-static: 0.0
- Accuracy Void-unclear: 0.0
- Iou Unlabeled: nan
- Iou Flat-road: 0.5131
- Iou Flat-sidewalk: 0.7717
- Iou Flat-crosswalk: 0.0
- Iou Flat-cyclinglane: 0.0013
- Iou Flat-parkingdriveway: 0.0024
- Iou Flat-railtrack: nan
- Iou Flat-curb: 0.0
- Iou Human-person: 0.0
- Iou Human-rider: 0.0
- Iou Vehicle-car: 0.5983
- Iou Vehicle-truck: 0.0
- Iou Vehicle-bus: 0.0
- Iou Vehicle-tramtrain: nan
- Iou Vehicle-motorcycle: 0.0
- Iou Vehicle-bicycle: 0.0
- Iou Vehicle-caravan: 0.0
- Iou Vehicle-cartrailer: 0.0
- Iou Construction-building: 0.5449
- Iou Construction-door: 0.0
- Iou Construction-wall: 0.0000
- Iou Construction-fenceguardrail: 0.0
- Iou Construction-bridge: 0.0
- Iou Construction-tunnel: nan
- Iou Construction-stairs: 0.0
- Iou Object-pole: 0.0
- Iou Object-trafficsign: 0.0
- Iou Object-trafficlight: 0.0
- Iou Nature-vegetation: 0.7519
- Iou Nature-terrain: 0.5340
- Iou Sky: 0.8151
- Iou Void-ground: 0.0
- Iou Void-dynamic: 0.0
- Iou Void-static: 0.0
- Iou Void-unclear: 0.0
## 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: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear |
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| 3.0226 | 0.05 | 20 | 3.2451 | 0.0770 | 0.1291 | 0.5814 | nan | 0.3392 | 0.9150 | 0.0007 | 0.0167 | 0.0052 | nan | 0.0281 | 0.0013 | 0.0 | 0.6316 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8880 | 0.0 | 0.0100 | 0.0 | 0.0 | nan | 0.0 | 0.0212 | 0.0 | 0.0 | 0.7776 | 0.2569 | 0.1047 | 0.0036 | 0.0021 | 0.0002 | 0.0 | 0.0 | 0.2887 | 0.6060 | 0.0006 | 0.0156 | 0.0051 | 0.0 | 0.0209 | 0.0011 | 0.0 | 0.4177 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3513 | 0.0 | 0.0087 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0109 | 0.0 | 0.0 | 0.6347 | 0.2251 | 0.1037 | 0.0027 | 0.0019 | 0.0002 | 0.0 |
| 2.4643 | 0.1 | 40 | 2.4748 | 0.0979 | 0.1462 | 0.6444 | nan | 0.6454 | 0.9084 | 0.0012 | 0.0002 | 0.0006 | nan | 0.0124 | 0.0000 | 0.0 | 0.6787 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8760 | 0.0 | 0.0119 | 0.0 | 0.0 | nan | 0.0 | 0.0038 | 0.0 | 0.0 | 0.9282 | 0.0365 | 0.4258 | 0.0016 | 0.0 | 0.0 | 0.0 | nan | 0.4331 | 0.6624 | 0.0012 | 0.0002 | 0.0006 | nan | 0.0114 | 0.0000 | 0.0 | 0.4718 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4327 | 0.0 | 0.0115 | 0.0 | 0.0 | nan | 0.0 | 0.0036 | 0.0 | 0.0 | 0.6481 | 0.0359 | 0.4181 | 0.0015 | 0.0 | 0.0 | 0.0 |
| 2.3866 | 0.15 | 60 | 2.0828 | 0.1129 | 0.1636 | 0.6679 | nan | 0.7570 | 0.8891 | 0.0000 | 0.0000 | 0.0002 | nan | 0.0010 | 0.0001 | 0.0 | 0.7980 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8425 | 0.0 | 0.0088 | 0.0 | 0.0 | nan | 0.0 | 0.0006 | 0.0 | 0.0 | 0.9416 | 0.3129 | 0.5187 | 0.0000 | 0.0 | 0.0 | 0.0 | nan | 0.4431 | 0.6874 | 0.0000 | 0.0000 | 0.0002 | nan | 0.0010 | 0.0001 | 0.0 | 0.5410 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4735 | 0.0 | 0.0087 | 0.0 | 0.0 | nan | 0.0 | 0.0006 | 0.0 | 0.0 | 0.6784 | 0.2726 | 0.5071 | 0.0000 | 0.0 | 0.0 | 0.0 |
| 2.4998 | 0.2 | 80 | 1.9122 | 0.1276 | 0.1772 | 0.6866 | nan | 0.8098 | 0.8856 | 0.0 | 0.0001 | 0.0 | nan | 0.0004 | 0.0 | 0.0 | 0.8752 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8497 | 0.0 | 0.0073 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9361 | 0.4185 | 0.7115 | 0.0000 | 0.0 | 0.0 | 0.0 | nan | 0.4611 | 0.7099 | 0.0 | 0.0001 | 0.0 | nan | 0.0004 | 0.0 | 0.0 | 0.5317 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5068 | 0.0 | 0.0072 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7000 | 0.3600 | 0.6771 | 0.0000 | 0.0 | 0.0 | 0.0 |
| 1.9775 | 0.25 | 100 | 1.7125 | 0.1344 | 0.1848 | 0.6979 | nan | 0.7868 | 0.9065 | 0.0 | 0.0002 | 0.0000 | nan | 0.0007 | 0.0 | 0.0 | 0.8211 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8331 | 0.0 | 0.0015 | 0.0 | 0.0 | nan | 0.0 | 0.0010 | 0.0 | 0.0 | 0.9286 | 0.6121 | 0.8377 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4865 | 0.7134 | 0.0 | 0.0002 | 0.0000 | nan | 0.0007 | 0.0 | 0.0 | 0.5603 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5044 | 0.0 | 0.0015 | 0.0 | 0.0 | nan | 0.0 | 0.0010 | 0.0 | 0.0 | 0.7089 | 0.4386 | 0.7511 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.6408 | 0.3 | 120 | 1.6293 | 0.1379 | 0.1888 | 0.7033 | nan | 0.7671 | 0.9293 | 0.0 | 0.0020 | 0.0000 | nan | 0.0002 | 0.0 | 0.0 | 0.8367 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8499 | 0.0 | 0.0005 | 0.0 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.8888 | 0.6973 | 0.8808 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4924 | 0.7106 | 0.0 | 0.0020 | 0.0000 | nan | 0.0002 | 0.0 | 0.0 | 0.5812 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5056 | 0.0 | 0.0005 | 0.0 | 0.0 | nan | 0.0 | 0.0001 | 0.0 | 0.0 | 0.7306 | 0.4774 | 0.7751 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2.0971 | 0.35 | 140 | 1.5878 | 0.1392 | 0.1931 | 0.7067 | nan | 0.8429 | 0.9084 | 0.0 | 0.0003 | 0.0000 | nan | 0.0000 | 0.0 | 0.0 | 0.8886 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8458 | 0.0 | 0.0061 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8806 | 0.7458 | 0.8668 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4799 | 0.7350 | 0.0 | 0.0003 | 0.0000 | nan | 0.0000 | 0.0 | 0.0 | 0.5623 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5298 | 0.0 | 0.0061 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7340 | 0.4897 | 0.7783 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.5524 | 0.4 | 160 | 1.5210 | 0.1416 | 0.1935 | 0.7104 | nan | 0.8431 | 0.9047 | 0.0 | 0.0054 | 0.0004 | nan | 0.0001 | 0.0 | 0.0 | 0.8147 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8864 | 0.0 | 0.0011 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8979 | 0.7542 | 0.8898 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4947 | 0.7378 | 0.0 | 0.0054 | 0.0004 | nan | 0.0001 | 0.0 | 0.0 | 0.6030 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5067 | 0.0 | 0.0011 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7376 | 0.5122 | 0.7895 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2.1125 | 0.45 | 180 | 1.4662 | 0.1381 | 0.1967 | 0.7038 | nan | 0.8346 | 0.9129 | 0.0 | 0.0013 | 0.0012 | nan | 0.0 | 0.0 | 0.0 | 0.8720 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8763 | 0.0 | 0.0004 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8097 | 0.8918 | 0.8970 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5026 | 0.7394 | 0.0 | 0.0013 | 0.0012 | nan | 0.0 | 0.0 | 0.0 | 0.5807 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5253 | 0.0 | 0.0004 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6976 | 0.4392 | 0.7937 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.7884 | 0.5 | 200 | 1.3982 | 0.1411 | 0.1928 | 0.7139 | nan | 0.8103 | 0.9245 | 0.0 | 0.0012 | 0.0007 | nan | 0.0 | 0.0 | 0.0 | 0.8626 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8615 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9163 | 0.6946 | 0.9044 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5111 | 0.7331 | 0.0 | 0.0012 | 0.0007 | nan | 0.0 | 0.0 | 0.0 | 0.5772 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5245 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7332 | 0.5028 | 0.7899 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.7399 | 0.55 | 220 | 1.4060 | 0.1429 | 0.1965 | 0.7154 | nan | 0.8177 | 0.9351 | 0.0 | 0.0000 | 0.0004 | nan | 0.0 | 0.0 | 0.0 | 0.8868 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8743 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8626 | 0.8036 | 0.9097 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5061 | 0.7372 | 0.0 | 0.0000 | 0.0004 | nan | 0.0 | 0.0 | 0.0 | 0.5900 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5170 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7513 | 0.5264 | 0.8019 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.6151 | 0.6 | 240 | 1.3772 | 0.1407 | 0.1920 | 0.7140 | nan | 0.8674 | 0.9061 | 0.0 | 0.0000 | 0.0018 | nan | 0.0 | 0.0 | 0.0 | 0.8325 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8259 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9415 | 0.6687 | 0.9074 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5026 | 0.7512 | 0.0 | 0.0000 | 0.0018 | nan | 0.0 | 0.0 | 0.0 | 0.5943 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5339 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7135 | 0.4782 | 0.7870 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.8311 | 0.65 | 260 | 1.3217 | 0.1418 | 0.1945 | 0.7189 | nan | 0.8499 | 0.9251 | 0.0 | 0.0002 | 0.0028 | nan | 0.0 | 0.0 | 0.0 | 0.8839 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8598 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9138 | 0.7105 | 0.8851 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5198 | 0.7509 | 0.0 | 0.0002 | 0.0028 | nan | 0.0 | 0.0 | 0.0 | 0.5533 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5311 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7431 | 0.4986 | 0.7975 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.215 | 0.7 | 280 | 1.3329 | 0.1434 | 0.1977 | 0.7195 | nan | 0.8756 | 0.9182 | 0.0 | 0.0003 | 0.0023 | nan | 0.0 | 0.0 | 0.0 | 0.8858 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9029 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8752 | 0.7868 | 0.8822 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5146 | 0.7624 | 0.0 | 0.0003 | 0.0023 | nan | 0.0 | 0.0 | 0.0 | 0.5919 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5143 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7533 | 0.5041 | 0.8033 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.5656 | 0.75 | 300 | 1.2993 | 0.1433 | 0.1973 | 0.7170 | nan | 0.8972 | 0.9030 | 0.0 | 0.0002 | 0.0016 | nan | 0.0 | 0.0 | 0.0 | 0.8611 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8344 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9037 | 0.8070 | 0.9082 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4916 | 0.7608 | 0.0 | 0.0002 | 0.0015 | nan | 0.0 | 0.0 | 0.0 | 0.5982 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5474 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7300 | 0.5160 | 0.7977 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.3712 | 0.8 | 320 | 1.2934 | 0.1445 | 0.1984 | 0.7203 | nan | 0.9047 | 0.9056 | 0.0 | 0.0004 | 0.0006 | nan | 0.0 | 0.0 | 0.0 | 0.8724 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8694 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8976 | 0.7999 | 0.8984 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4941 | 0.7696 | 0.0 | 0.0004 | 0.0006 | nan | 0.0 | 0.0 | 0.0 | 0.5955 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5460 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7442 | 0.5189 | 0.8093 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.1831 | 0.85 | 340 | 1.2771 | 0.1453 | 0.1996 | 0.7217 | nan | 0.9035 | 0.9105 | 0.0 | 0.0010 | 0.0012 | nan | 0.0 | 0.0 | 0.0 | 0.8679 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8874 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8812 | 0.8507 | 0.8838 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4996 | 0.7710 | 0.0 | 0.0010 | 0.0012 | nan | 0.0 | 0.0 | 0.0 | 0.6037 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5458 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7443 | 0.5249 | 0.8129 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.343 | 0.9 | 360 | 1.2465 | 0.1449 | 0.1989 | 0.7212 | nan | 0.9086 | 0.9032 | 0.0 | 0.0007 | 0.0022 | nan | 0.0 | 0.0 | 0.0 | 0.8650 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8673 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9040 | 0.8253 | 0.8911 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4947 | 0.7732 | 0.0 | 0.0007 | 0.0022 | nan | 0.0 | 0.0 | 0.0 | 0.5988 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5508 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7403 | 0.5220 | 0.8099 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4857 | 0.95 | 380 | 1.2733 | 0.1453 | 0.2008 | 0.7241 | nan | 0.8789 | 0.9317 | 0.0 | 0.0019 | 0.0035 | nan | 0.0 | 0.0 | 0.0 | 0.8861 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9032 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8505 | 0.8620 | 0.9060 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5280 | 0.7656 | 0.0 | 0.0019 | 0.0035 | nan | 0.0 | 0.0 | 0.0 | 0.5952 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5299 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7500 | 0.5148 | 0.8150 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.1595 | 1.0 | 400 | 1.2757 | 0.1462 | 0.2006 | 0.7257 | nan | 0.8968 | 0.9232 | 0.0 | 0.0013 | 0.0024 | nan | 0.0 | 0.0 | 0.0 | 0.8803 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8822 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8802 | 0.8441 | 0.9068 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5131 | 0.7717 | 0.0 | 0.0013 | 0.0024 | nan | 0.0 | 0.0 | 0.0 | 0.5983 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5449 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7519 | 0.5340 | 0.8151 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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