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---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-sidewalk-oct-22
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-finetuned-segments-sidewalk-oct-22
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: 1.1574
- Mean Iou: 0.1657
- Mean Accuracy: 0.2143
- Overall Accuracy: 0.7508
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.8145
- Accuracy Flat-sidewalk: 0.9504
- Accuracy Flat-crosswalk: 0.0
- Accuracy Flat-cyclinglane: 0.5828
- Accuracy Flat-parkingdriveway: 0.0118
- Accuracy Flat-railtrack: nan
- Accuracy Flat-curb: 0.0001
- Accuracy Human-person: 0.0
- Accuracy Human-rider: 0.0
- Accuracy Vehicle-car: 0.8895
- Accuracy Vehicle-truck: 0.0
- Accuracy Vehicle-bus: 0.0
- Accuracy Vehicle-tramtrain: 0.0
- Accuracy Vehicle-motorcycle: 0.0
- Accuracy Vehicle-bicycle: 0.0
- Accuracy Vehicle-caravan: 0.0
- Accuracy Vehicle-cartrailer: 0.0
- Accuracy Construction-building: 0.8937
- Accuracy Construction-door: 0.0
- Accuracy Construction-wall: 0.0389
- 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: 0.9244
- Accuracy Nature-terrain: 0.8287
- Accuracy Sky: 0.9224
- 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: 0.5381
- Iou Flat-sidewalk: 0.7939
- Iou Flat-crosswalk: 0.0
- Iou Flat-cyclinglane: 0.5124
- Iou Flat-parkingdriveway: 0.0117
- Iou Flat-railtrack: nan
- Iou Flat-curb: 0.0001
- Iou Human-person: 0.0
- Iou Human-rider: 0.0
- Iou Vehicle-car: 0.6117
- Iou Vehicle-truck: 0.0
- Iou Vehicle-bus: 0.0
- Iou Vehicle-tramtrain: 0.0
- Iou Vehicle-motorcycle: 0.0
- Iou Vehicle-bicycle: 0.0
- Iou Vehicle-caravan: 0.0
- Iou Vehicle-cartrailer: 0.0
- Iou Construction-building: 0.5570
- Iou Construction-door: 0.0
- Iou Construction-wall: 0.0365
- 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: 0.7504
- Iou Nature-terrain: 0.6347
- Iou Sky: 0.8566
- 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### 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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| 2.6835 | 0.25 | 20 | 2.9300 | 0.0679 | 0.1189 | 0.5663 | nan | 0.0864 | 0.9582 | 0.0005 | 0.0171 | 0.0000 | nan | 0.0049 | 0.0042 | 0.0 | 0.8284 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7913 | 0.0 | 0.0535 | 0.0 | 0.0 | nan | 0.0029 | 0.0 | 0.0 | 0.0 | 0.9260 | 0.0016 | 0.1138 | 0.0168 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0824 | 0.5815 | 0.0005 | 0.0168 | 0.0000 | 0.0 | 0.0045 | 0.0040 | 0.0 | 0.5349 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4154 | 0.0 | 0.0414 | 0.0 | 0.0 | 0.0 | 0.0006 | 0.0 | 0.0 | 0.0 | 0.5765 | 0.0016 | 0.1137 | 0.0025 | 0.0 | 0.0 | 0.0 |
| 2.3707 | 0.5 | 40 | 2.1968 | 0.0875 | 0.1387 | 0.6231 | nan | 0.5613 | 0.9361 | 0.0 | 0.0210 | 0.0003 | nan | 0.0019 | 0.0 | 0.0 | 0.7965 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8356 | 0.0 | 0.0281 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9513 | 0.0032 | 0.3006 | 0.0011 | 0.0 | 0.0 | 0.0 | nan | 0.3878 | 0.6646 | 0.0 | 0.0208 | 0.0003 | 0.0 | 0.0019 | 0.0 | 0.0 | 0.5587 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4439 | 0.0 | 0.0249 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5675 | 0.0032 | 0.3003 | 0.0009 | 0.0 | 0.0 | 0.0 |
| 2.0797 | 0.75 | 60 | 1.9742 | 0.1106 | 0.1579 | 0.6543 | nan | 0.7248 | 0.9240 | 0.0 | 0.0036 | 0.0008 | nan | 0.0000 | 0.0 | 0.0 | 0.8368 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8428 | 0.0 | 0.0254 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9498 | 0.0444 | 0.7003 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4154 | 0.7036 | 0.0 | 0.0036 | 0.0008 | nan | 0.0000 | 0.0 | 0.0 | 0.5498 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4939 | 0.0 | 0.0240 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6151 | 0.0426 | 0.6908 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.9067 | 1.0 | 80 | 1.7288 | 0.1234 | 0.1699 | 0.6749 | nan | 0.7502 | 0.9269 | 0.0 | 0.0766 | 0.0014 | nan | 0.0000 | 0.0 | 0.0 | 0.8214 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8719 | 0.0 | 0.0111 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9451 | 0.2194 | 0.8139 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4498 | 0.7152 | 0.0 | 0.0761 | 0.0013 | nan | 0.0000 | 0.0 | 0.0 | 0.5696 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5007 | 0.0 | 0.0108 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6444 | 0.1954 | 0.7863 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.6673 | 1.25 | 100 | 1.6680 | 0.1258 | 0.1752 | 0.6789 | nan | 0.8230 | 0.9033 | 0.0 | 0.0493 | 0.0022 | nan | 0.0000 | 0.0 | 0.0 | 0.8676 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8389 | 0.0 | 0.0075 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9609 | 0.2915 | 0.8618 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4254 | 0.7481 | 0.0 | 0.0488 | 0.0022 | nan | 0.0000 | 0.0 | 0.0 | 0.5539 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5266 | 0.0 | 0.0074 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6400 | 0.2581 | 0.8161 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.7134 | 1.5 | 120 | 1.5470 | 0.1376 | 0.1870 | 0.6997 | nan | 0.7864 | 0.9287 | 0.0 | 0.1234 | 0.0027 | nan | 0.0000 | 0.0 | 0.0 | 0.8766 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8540 | 0.0 | 0.0060 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9433 | 0.5618 | 0.9008 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4735 | 0.7320 | 0.0 | 0.1216 | 0.0027 | nan | 0.0000 | 0.0 | 0.0 | 0.5599 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5319 | 0.0 | 0.0059 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6897 | 0.4601 | 0.8255 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4384 | 1.75 | 140 | 1.4997 | 0.1458 | 0.1931 | 0.7143 | nan | 0.7973 | 0.9446 | 0.0 | 0.2050 | 0.0025 | nan | 0.0 | 0.0 | 0.0 | 0.8691 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8695 | 0.0 | 0.0025 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9304 | 0.6705 | 0.8889 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4842 | 0.7479 | 0.0 | 0.1960 | 0.0025 | nan | 0.0 | 0.0 | 0.0 | 0.5860 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5294 | 0.0 | 0.0025 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7244 | 0.5600 | 0.8314 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4101 | 2.0 | 160 | 1.4325 | 0.1485 | 0.1990 | 0.7167 | nan | 0.8247 | 0.9212 | 0.0 | 0.2410 | 0.0032 | nan | 0.0 | 0.0 | 0.0 | 0.8787 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8896 | 0.0 | 0.0042 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9131 | 0.7902 | 0.9017 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4675 | 0.7648 | 0.0 | 0.2283 | 0.0032 | nan | 0.0 | 0.0 | 0.0 | 0.5777 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5259 | 0.0 | 0.0041 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7388 | 0.6051 | 0.8352 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4613 | 2.25 | 180 | 1.3689 | 0.1522 | 0.2012 | 0.7248 | nan | 0.7783 | 0.9430 | 0.0 | 0.3362 | 0.0037 | nan | 0.0 | 0.0 | 0.0 | 0.8668 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8788 | 0.0 | 0.0033 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9201 | 0.7933 | 0.9142 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4951 | 0.7564 | 0.0 | 0.3089 | 0.0037 | nan | 0.0 | 0.0 | 0.0 | 0.5926 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5366 | 0.0 | 0.0033 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7330 | 0.6059 | 0.8348 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.1652 | 2.5 | 200 | 1.3458 | 0.1566 | 0.2036 | 0.7323 | nan | 0.7605 | 0.9551 | 0.0 | 0.4259 | 0.0038 | nan | 0.0 | 0.0 | 0.0 | 0.8580 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8902 | 0.0 | 0.0066 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9193 | 0.7943 | 0.9027 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5290 | 0.7548 | 0.0 | 0.3710 | 0.0038 | nan | 0.0 | 0.0 | 0.0 | 0.6064 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5270 | 0.0 | 0.0065 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7467 | 0.6256 | 0.8409 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.3201 | 2.75 | 220 | 1.2652 | 0.1572 | 0.2057 | 0.7353 | nan | 0.7825 | 0.9559 | 0.0 | 0.4231 | 0.0056 | nan | 0.0000 | 0.0 | 0.0 | 0.8904 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8834 | 0.0 | 0.0122 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9104 | 0.8184 | 0.9014 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5340 | 0.7600 | 0.0 | 0.3872 | 0.0056 | nan | 0.0000 | 0.0 | 0.0 | 0.5909 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5480 | 0.0 | 0.0120 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7383 | 0.6066 | 0.8463 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.2234 | 3.0 | 240 | 1.2746 | 0.1594 | 0.2088 | 0.7376 | nan | 0.8409 | 0.9292 | 0.0 | 0.4723 | 0.0054 | nan | 0.0 | 0.0 | 0.0 | 0.8857 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8767 | 0.0 | 0.0206 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9318 | 0.7948 | 0.9251 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4920 | 0.7916 | 0.0 | 0.4224 | 0.0053 | nan | 0.0 | 0.0 | 0.0 | 0.6083 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5504 | 0.0 | 0.0199 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7396 | 0.6269 | 0.8444 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4557 | 3.25 | 260 | 1.2698 | 0.1584 | 0.2075 | 0.7313 | nan | 0.8659 | 0.9053 | 0.0 | 0.4557 | 0.0059 | nan | 0.0 | 0.0 | 0.0 | 0.8611 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8783 | 0.0 | 0.0141 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9394 | 0.7982 | 0.9172 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4660 | 0.7939 | 0.0 | 0.4017 | 0.0059 | nan | 0.0 | 0.0 | 0.0 | 0.6271 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5439 | 0.0 | 0.0137 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7346 | 0.6333 | 0.8478 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.2238 | 3.5 | 280 | 1.2213 | 0.1610 | 0.2090 | 0.7427 | nan | 0.8092 | 0.9490 | 0.0 | 0.4971 | 0.0087 | nan | 0.0 | 0.0 | 0.0 | 0.8902 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8860 | 0.0 | 0.0207 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9339 | 0.7840 | 0.9103 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5269 | 0.7830 | 0.0 | 0.4531 | 0.0086 | nan | 0.0 | 0.0 | 0.0 | 0.5897 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5528 | 0.0 | 0.0200 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7379 | 0.6292 | 0.8511 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.066 | 3.75 | 300 | 1.1935 | 0.1624 | 0.2109 | 0.7442 | nan | 0.8253 | 0.9479 | 0.0 | 0.5012 | 0.0083 | nan | 0.0 | 0.0 | 0.0 | 0.8755 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8923 | 0.0 | 0.0307 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9137 | 0.8313 | 0.9234 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5267 | 0.7849 | 0.0 | 0.4499 | 0.0082 | nan | 0.0 | 0.0 | 0.0 | 0.6217 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5554 | 0.0 | 0.0294 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7463 | 0.6202 | 0.8525 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.1549 | 4.0 | 320 | 1.1899 | 0.1632 | 0.2118 | 0.7464 | nan | 0.8334 | 0.9423 | 0.0 | 0.5290 | 0.0073 | nan | 0.0000 | 0.0 | 0.0 | 0.8857 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8956 | 0.0 | 0.0194 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9280 | 0.8115 | 0.9247 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5215 | 0.7940 | 0.0 | 0.4730 | 0.0073 | nan | 0.0000 | 0.0 | 0.0 | 0.6143 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5530 | 0.0 | 0.0188 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7488 | 0.6382 | 0.8541 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0461 | 4.25 | 340 | 1.1746 | 0.1643 | 0.2127 | 0.7482 | nan | 0.8171 | 0.9549 | 0.0 | 0.5455 | 0.0078 | nan | 0.0 | 0.0 | 0.0 | 0.8974 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8812 | 0.0 | 0.0387 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9161 | 0.8302 | 0.9174 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5322 | 0.7872 | 0.0 | 0.4917 | 0.0077 | nan | 0.0 | 0.0 | 0.0 | 0.6076 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5651 | 0.0 | 0.0366 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7456 | 0.6282 | 0.8553 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0398 | 4.5 | 360 | 1.1687 | 0.1652 | 0.2128 | 0.7494 | nan | 0.8254 | 0.9501 | 0.0 | 0.5507 | 0.0094 | nan | 0.0000 | 0.0 | 0.0 | 0.8755 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8972 | 0.0 | 0.0341 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9253 | 0.8186 | 0.9232 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5323 | 0.7930 | 0.0 | 0.4922 | 0.0093 | nan | 0.0000 | 0.0 | 0.0 | 0.6277 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5531 | 0.0 | 0.0323 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7518 | 0.6382 | 0.8553 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0538 | 4.75 | 380 | 1.1675 | 0.1655 | 0.2135 | 0.7499 | nan | 0.8351 | 0.9458 | 0.0 | 0.5657 | 0.0087 | nan | 0.0000 | 0.0 | 0.0 | 0.8830 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8980 | 0.0 | 0.0322 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9244 | 0.8123 | 0.9262 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5260 | 0.7971 | 0.0 | 0.5002 | 0.0086 | nan | 0.0000 | 0.0 | 0.0 | 0.6214 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5537 | 0.0 | 0.0305 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7554 | 0.6472 | 0.8564 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.0232 | 5.0 | 400 | 1.1574 | 0.1657 | 0.2143 | 0.7508 | nan | 0.8145 | 0.9504 | 0.0 | 0.5828 | 0.0118 | nan | 0.0001 | 0.0 | 0.0 | 0.8895 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8937 | 0.0 | 0.0389 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9244 | 0.8287 | 0.9224 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5381 | 0.7939 | 0.0 | 0.5124 | 0.0117 | nan | 0.0001 | 0.0 | 0.0 | 0.6117 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5570 | 0.0 | 0.0365 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7504 | 0.6347 | 0.8566 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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