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--- |
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license: other |
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base_model: nvidia/mit-b0 |
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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-segments-sidewalk-2 |
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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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# segformer-b0-finetuned-segments-sidewalk-2 |
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This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5973 |
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- Mean Iou: 0.2741 |
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- Mean Accuracy: 0.3241 |
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- Overall Accuracy: 0.8431 |
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- Accuracy Unlabeled: nan |
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- Accuracy Flat-road: 0.8917 |
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- Accuracy Flat-sidewalk: 0.9574 |
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- Accuracy Flat-crosswalk: 0.5943 |
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- Accuracy Flat-cyclinglane: 0.7718 |
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- Accuracy Flat-parkingdriveway: 0.4461 |
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- Accuracy Flat-railtrack: 0.0 |
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- Accuracy Flat-curb: 0.4790 |
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- Accuracy Human-person: 0.6275 |
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- Accuracy Human-rider: 0.0 |
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- Accuracy Vehicle-car: 0.9331 |
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- Accuracy Vehicle-truck: 0.0 |
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- Accuracy Vehicle-bus: 0.0 |
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- Accuracy Vehicle-tramtrain: 0.0 |
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- Accuracy Vehicle-motorcycle: 0.0 |
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- Accuracy Vehicle-bicycle: 0.3002 |
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- Accuracy Vehicle-caravan: 0.0 |
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- Accuracy Vehicle-cartrailer: 0.0 |
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- Accuracy Construction-building: 0.8853 |
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- Accuracy Construction-door: 0.0 |
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- Accuracy Construction-wall: 0.5059 |
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- Accuracy Construction-fenceguardrail: 0.1500 |
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- Accuracy Construction-bridge: 0.0 |
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- Accuracy Construction-tunnel: nan |
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- Accuracy Construction-stairs: 0.0 |
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- Accuracy Object-pole: 0.1196 |
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- Accuracy Object-trafficsign: 0.0 |
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- Accuracy Object-trafficlight: 0.0 |
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- Accuracy Nature-vegetation: 0.9324 |
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- Accuracy Nature-terrain: 0.8845 |
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- Accuracy Sky: 0.9657 |
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- Accuracy Void-ground: 0.0 |
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- Accuracy Void-dynamic: 0.0 |
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- Accuracy Void-static: 0.2504 |
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- Accuracy Void-unclear: 0.0 |
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- Iou Unlabeled: nan |
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- Iou Flat-road: 0.7433 |
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- Iou Flat-sidewalk: 0.8594 |
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- Iou Flat-crosswalk: 0.5654 |
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- Iou Flat-cyclinglane: 0.7149 |
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- Iou Flat-parkingdriveway: 0.3468 |
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- Iou Flat-railtrack: 0.0 |
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- Iou Flat-curb: 0.3889 |
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- Iou Human-person: 0.4411 |
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- Iou Human-rider: 0.0 |
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- Iou Vehicle-car: 0.8003 |
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- Iou Vehicle-truck: 0.0 |
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- Iou Vehicle-bus: 0.0 |
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- Iou Vehicle-tramtrain: 0.0 |
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- Iou Vehicle-motorcycle: 0.0 |
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- Iou Vehicle-bicycle: 0.2484 |
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- Iou Vehicle-caravan: 0.0 |
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- Iou Vehicle-cartrailer: 0.0 |
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- Iou Construction-building: 0.7020 |
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- Iou Construction-door: 0.0 |
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- Iou Construction-wall: 0.3589 |
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- Iou Construction-fenceguardrail: 0.1472 |
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- Iou Construction-bridge: 0.0 |
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- Iou Construction-tunnel: nan |
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- Iou Construction-stairs: 0.0 |
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- Iou Object-pole: 0.1109 |
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- Iou Object-trafficsign: 0.0 |
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- Iou Object-trafficlight: 0.0 |
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- Iou Nature-vegetation: 0.8106 |
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- Iou Nature-terrain: 0.7138 |
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- Iou Sky: 0.8976 |
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- Iou Void-ground: 0.0 |
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- Iou Void-dynamic: 0.0 |
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- Iou Void-static: 0.1950 |
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- Iou Void-unclear: 0.0 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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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: 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: 10 |
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### Training results |
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| 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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| 1.611 | 1.0 | 100 | 1.3435 | 0.1511 | 0.1998 | 0.7326 | nan | 0.8304 | 0.8942 | 0.0 | 0.4356 | 0.0015 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.9156 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8185 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9098 | 0.8566 | 0.9324 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4820 | 0.7633 | 0.0 | 0.4233 | 0.0015 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.5840 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5841 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7275 | 0.5762 | 0.8456 | 0.0 | 0.0 | 0.0 | 0.0 | |
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| 1.137 | 2.0 | 200 | 1.0101 | 0.1634 | 0.2060 | 0.7605 | nan | 0.8276 | 0.9403 | 0.0 | 0.5388 | 0.0501 | 0.0 | 0.0037 | 0.0 | 0.0 | 0.9244 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8842 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9359 | 0.7548 | 0.9394 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5394 | 0.7902 | 0.0 | 0.5030 | 0.0473 | 0.0 | 0.0037 | 0.0 | 0.0 | 0.6562 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5962 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7557 | 0.6370 | 0.8647 | 0.0 | 0.0 | 0.0 | 0.0 | |
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| 0.9072 | 3.0 | 300 | 0.8578 | 0.1838 | 0.2268 | 0.7835 | nan | 0.8225 | 0.9528 | 0.0 | 0.5973 | 0.2935 | 0.0 | 0.2630 | 0.0025 | 0.0 | 0.9198 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8787 | 0.0 | 0.0079 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9360 | 0.8653 | 0.9461 | 0.0 | 0.0 | 0.0001 | 0.0 | nan | 0.6292 | 0.8005 | 0.0 | 0.5630 | 0.2090 | 0.0 | 0.2160 | 0.0025 | 0.0 | 0.6917 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6191 | 0.0 | 0.0079 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7644 | 0.6802 | 0.8816 | 0.0 | 0.0 | 0.0001 | 0.0 | |
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| 0.8185 | 4.0 | 400 | 0.7882 | 0.1989 | 0.2415 | 0.7956 | nan | 0.7952 | 0.9597 | 0.0 | 0.7546 | 0.2634 | 0.0 | 0.3463 | 0.0840 | 0.0 | 0.9316 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0041 | 0.0 | 0.0 | 0.8908 | 0.0 | 0.1958 | 0.0 | 0.0 | nan | 0.0 | 0.0022 | 0.0 | 0.0 | 0.9252 | 0.8699 | 0.9444 | 0.0 | 0.0 | 0.0031 | 0.0 | nan | 0.6240 | 0.8069 | 0.0 | 0.6760 | 0.2169 | 0.0 | 0.2836 | 0.0834 | 0.0 | 0.6913 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0041 | 0.0 | 0.0 | 0.6411 | 0.0 | 0.1802 | 0.0 | 0.0 | nan | 0.0 | 0.0022 | 0.0 | 0.0 | 0.7785 | 0.6854 | 0.8880 | 0.0 | 0.0 | 0.0030 | 0.0 | |
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| 0.7126 | 5.0 | 500 | 0.6955 | 0.2183 | 0.2633 | 0.8120 | nan | 0.8911 | 0.9470 | 0.0 | 0.7170 | 0.4330 | 0.0 | 0.3882 | 0.3402 | 0.0 | 0.9353 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0431 | 0.0 | 0.0 | 0.9055 | 0.0 | 0.3163 | 0.0001 | 0.0 | nan | 0.0 | 0.0169 | 0.0 | 0.0 | 0.9358 | 0.8452 | 0.9570 | 0.0 | 0.0 | 0.0175 | 0.0 | nan | 0.6663 | 0.8443 | 0.0 | 0.6712 | 0.3070 | 0.0 | 0.3186 | 0.2908 | 0.0 | 0.7295 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0426 | 0.0 | 0.0 | 0.6535 | 0.0 | 0.2567 | 0.0001 | 0.0 | nan | 0.0 | 0.0168 | 0.0 | 0.0 | 0.7948 | 0.7024 | 0.8913 | 0.0 | 0.0 | 0.0169 | 0.0 | |
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| 0.6301 | 6.0 | 600 | 0.6489 | 0.2343 | 0.2820 | 0.8200 | nan | 0.8983 | 0.9516 | 0.0367 | 0.6998 | 0.4508 | 0.0 | 0.4324 | 0.4498 | 0.0 | 0.9273 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1090 | 0.0 | 0.0 | 0.8946 | 0.0 | 0.4651 | 0.0242 | 0.0 | nan | 0.0 | 0.0529 | 0.0 | 0.0 | 0.9303 | 0.8783 | 0.9598 | 0.0 | 0.0 | 0.1467 | 0.0 | nan | 0.6706 | 0.8600 | 0.0366 | 0.6645 | 0.3249 | 0.0 | 0.3476 | 0.3571 | 0.0 | 0.7710 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1062 | 0.0 | 0.0 | 0.6826 | 0.0 | 0.3357 | 0.0242 | 0.0 | nan | 0.0 | 0.0517 | 0.0 | 0.0 | 0.7885 | 0.6900 | 0.8949 | 0.0 | 0.0 | 0.1269 | 0.0 | |
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| 0.6233 | 7.0 | 700 | 0.6114 | 0.2574 | 0.3056 | 0.8362 | nan | 0.9201 | 0.9481 | 0.4433 | 0.7484 | 0.4279 | 0.0 | 0.4697 | 0.5517 | 0.0 | 0.9392 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1717 | 0.0 | 0.0 | 0.9109 | 0.0 | 0.4857 | 0.0654 | 0.0 | nan | 0.0 | 0.0839 | 0.0 | 0.0 | 0.9306 | 0.8631 | 0.9602 | 0.0 | 0.0 | 0.1655 | 0.0 | nan | 0.7205 | 0.8672 | 0.4324 | 0.7056 | 0.3159 | 0.0 | 0.3665 | 0.4028 | 0.0 | 0.7768 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1553 | 0.0 | 0.0 | 0.6845 | 0.0 | 0.3553 | 0.0652 | 0.0 | nan | 0.0 | 0.0801 | 0.0 | 0.0 | 0.8115 | 0.7151 | 0.8973 | 0.0 | 0.0 | 0.1422 | 0.0 | |
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| 0.5976 | 8.0 | 800 | 0.6079 | 0.2675 | 0.3160 | 0.8399 | nan | 0.8925 | 0.9563 | 0.5797 | 0.7454 | 0.4506 | 0.0 | 0.4743 | 0.5701 | 0.0 | 0.9310 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2390 | 0.0 | 0.0 | 0.8996 | 0.0 | 0.5096 | 0.1015 | 0.0 | nan | 0.0 | 0.0911 | 0.0 | 0.0 | 0.9289 | 0.8862 | 0.9600 | 0.0 | 0.0 | 0.2112 | 0.0 | nan | 0.7371 | 0.8592 | 0.5545 | 0.7028 | 0.3314 | 0.0 | 0.3883 | 0.4237 | 0.0 | 0.7920 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2045 | 0.0 | 0.0 | 0.6950 | 0.0 | 0.3606 | 0.1006 | 0.0 | nan | 0.0 | 0.0868 | 0.0 | 0.0 | 0.8094 | 0.7093 | 0.8986 | 0.0 | 0.0 | 0.1743 | 0.0 | |
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| 0.5346 | 9.0 | 900 | 0.5961 | 0.2715 | 0.3233 | 0.8428 | nan | 0.9151 | 0.9464 | 0.5468 | 0.7937 | 0.4682 | 0.0 | 0.4628 | 0.6180 | 0.0 | 0.9350 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2914 | 0.0 | 0.0 | 0.8768 | 0.0 | 0.5235 | 0.1304 | 0.0 | nan | 0.0 | 0.1123 | 0.0 | 0.0 | 0.9369 | 0.8892 | 0.9621 | 0.0 | 0.0 | 0.2611 | 0.0 | nan | 0.7454 | 0.8704 | 0.5242 | 0.7242 | 0.3413 | 0.0 | 0.3770 | 0.4351 | 0.0 | 0.7947 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2413 | 0.0 | 0.0 | 0.7017 | 0.0 | 0.3608 | 0.1287 | 0.0 | nan | 0.0 | 0.1054 | 0.0 | 0.0 | 0.8056 | 0.7052 | 0.8994 | 0.0 | 0.0 | 0.1976 | 0.0 | |
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| 0.5377 | 10.0 | 1000 | 0.5973 | 0.2741 | 0.3241 | 0.8431 | nan | 0.8917 | 0.9574 | 0.5943 | 0.7718 | 0.4461 | 0.0 | 0.4790 | 0.6275 | 0.0 | 0.9331 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3002 | 0.0 | 0.0 | 0.8853 | 0.0 | 0.5059 | 0.1500 | 0.0 | nan | 0.0 | 0.1196 | 0.0 | 0.0 | 0.9324 | 0.8845 | 0.9657 | 0.0 | 0.0 | 0.2504 | 0.0 | nan | 0.7433 | 0.8594 | 0.5654 | 0.7149 | 0.3468 | 0.0 | 0.3889 | 0.4411 | 0.0 | 0.8003 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2484 | 0.0 | 0.0 | 0.7020 | 0.0 | 0.3589 | 0.1472 | 0.0 | nan | 0.0 | 0.1109 | 0.0 | 0.0 | 0.8106 | 0.7138 | 0.8976 | 0.0 | 0.0 | 0.1950 | 0.0 | |
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### Framework versions |
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- Transformers 4.38.1 |
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- Pytorch 2.2.1+cu118 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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