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segformer-finetuned-segments-opit

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  1. README.md +147 -0
  2. config.json +144 -0
  3. model.safetensors +3 -0
  4. runs/Nov21_19-39-00_c588f2716cd5/events.out.tfevents.1700595639.c588f2716cd5.633.0 +3 -0
  5. training_args.bin +3 -0
README.md ADDED
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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-finetuned-segments-opit
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+ results: []
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+ ---
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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-finetuned-segments-opit
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+
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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: 1.3483
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+ - Mean Iou: 0.1474
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+ - Mean Accuracy: 0.1958
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+ - Overall Accuracy: 0.7227
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Flat-road: 0.7892
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+ - Accuracy Flat-sidewalk: 0.9075
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+ - Accuracy Flat-crosswalk: 0.0
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+ - Accuracy Flat-cyclinglane: 0.1029
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+ - Accuracy Flat-parkingdriveway: 0.0001
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+ - Accuracy Flat-railtrack: nan
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+ - Accuracy Flat-curb: 0.0
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+ - Accuracy Human-person: 0.0
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+ - Accuracy Human-rider: 0.0
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+ - Accuracy Vehicle-car: 0.8749
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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: nan
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+ - Accuracy Vehicle-motorcycle: 0.0
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+ - Accuracy Vehicle-bicycle: 0.0
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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: 0.8943
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+ - Accuracy Construction-door: 0.0
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+ - Accuracy Construction-wall: 0.0
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+ - Accuracy Construction-fenceguardrail: 0.0
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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: 0.0
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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: 0.9118
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+ - Accuracy Nature-terrain: 0.6546
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+ - Accuracy Sky: 0.9352
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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: 0.0
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+ - Accuracy Void-unclear: 0.0
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+ - Iou Unlabeled: nan
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+ - Iou Flat-road: 0.4282
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+ - Iou Flat-sidewalk: 0.7768
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+ - Iou Flat-crosswalk: 0.0
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+ - Iou Flat-cyclinglane: 0.1021
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+ - Iou Flat-parkingdriveway: 0.0001
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+ - Iou Flat-railtrack: nan
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+ - Iou Flat-curb: 0.0
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+ - Iou Human-person: 0.0
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+ - Iou Human-rider: 0.0
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+ - Iou Vehicle-car: 0.6372
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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: nan
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+ - Iou Vehicle-motorcycle: 0.0
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+ - Iou Vehicle-bicycle: 0.0
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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: 0.5530
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+ - Iou Construction-door: 0.0
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+ - Iou Construction-wall: 0.0
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+ - Iou Construction-fenceguardrail: 0.0
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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: 0.0
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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: 0.7392
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+ - Iou Nature-terrain: 0.5009
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+ - Iou Sky: 0.8328
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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: 0.0
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+ - Iou Void-unclear: 0.0
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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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+
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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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+
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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: 2
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+ - eval_batch_size: 2
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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: 1
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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 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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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:|
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+ | 2.8103 | 0.06 | 25 | 3.0462 | 0.0835 | 0.1351 | 0.5790 | nan | 0.1892 | 0.9330 | 0.0 | 0.0048 | 0.0006 | nan | 0.0005 | 0.0002 | 0.0 | 0.5793 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8849 | 0.0 | 0.0000 | 0.0002 | 0.0 | nan | 0.0003 | 0.0009 | 0.0 | 0.0 | 0.6599 | 0.4398 | 0.4779 | 0.0004 | 0.0 | 0.0151 | 0.0 | 0.0 | 0.1368 | 0.6091 | 0.0 | 0.0046 | 0.0006 | 0.0 | 0.0005 | 0.0002 | 0.0 | 0.4916 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.3537 | 0.0 | 0.0000 | 0.0002 | 0.0 | 0.0 | 0.0003 | 0.0008 | 0.0 | 0.0 | 0.5851 | 0.2698 | 0.4572 | 0.0004 | 0.0 | 0.0123 | 0.0 |
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+ | 2.3833 | 0.12 | 50 | 2.3708 | 0.1025 | 0.1539 | 0.6295 | nan | 0.6185 | 0.8261 | 0.0 | 0.0007 | 0.0003 | nan | 0.0000 | 0.0 | 0.0 | 0.7749 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9283 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8688 | 0.0568 | 0.6944 | 0.0 | 0.0 | 0.0016 | 0.0 | nan | 0.3347 | 0.6672 | 0.0 | 0.0007 | 0.0003 | nan | 0.0000 | 0.0 | 0.0 | 0.5428 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4369 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6274 | 0.0451 | 0.6247 | 0.0 | 0.0 | 0.0015 | 0.0 |
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+ | 2.1946 | 0.19 | 75 | 1.9680 | 0.1145 | 0.1612 | 0.6615 | nan | 0.6725 | 0.8726 | 0.0 | 0.0011 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.7496 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9138 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9185 | 0.0333 | 0.8355 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.3738 | 0.7037 | 0.0 | 0.0011 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.5385 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.4836 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6493 | 0.0311 | 0.7684 | 0.0 | 0.0 | 0.0000 | 0.0 |
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+ | 1.959 | 0.25 | 100 | 1.8828 | 0.1179 | 0.1636 | 0.6727 | nan | 0.6801 | 0.8928 | 0.0 | 0.0006 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.7736 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8784 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9482 | 0.0389 | 0.8584 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.3884 | 0.7151 | 0.0 | 0.0006 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.5705 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5160 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6418 | 0.0360 | 0.7878 | 0.0 | 0.0 | 0.0000 | 0.0 |
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+ | 1.8759 | 0.31 | 125 | 1.7092 | 0.1260 | 0.1758 | 0.6862 | nan | 0.7247 | 0.8976 | 0.0 | 0.0000 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.9094 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8479 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9024 | 0.2625 | 0.9054 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3977 | 0.7307 | 0.0 | 0.0000 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.5237 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5329 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6911 | 0.2245 | 0.8065 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 2.0333 | 0.38 | 150 | 1.5558 | 0.1267 | 0.1751 | 0.6898 | nan | 0.7565 | 0.8952 | 0.0 | 0.0007 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.8792 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8874 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9104 | 0.1928 | 0.9055 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4083 | 0.7455 | 0.0 | 0.0007 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.5928 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5267 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6820 | 0.1637 | 0.8069 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.8985 | 0.44 | 175 | 1.5370 | 0.1277 | 0.1752 | 0.6939 | nan | 0.7438 | 0.9025 | 0.0 | 0.0068 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.8237 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8984 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9307 | 0.1976 | 0.9266 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4223 | 0.7553 | 0.0 | 0.0068 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.6077 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5131 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6724 | 0.1691 | 0.8133 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.7908 | 0.5 | 200 | 1.4854 | 0.1339 | 0.1843 | 0.7020 | nan | 0.7612 | 0.9068 | 0.0 | 0.0012 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.9215 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8381 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9078 | 0.4480 | 0.9274 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4095 | 0.7591 | 0.0 | 0.0012 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.5471 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5386 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7168 | 0.3673 | 0.8119 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.4371 | 0.56 | 225 | 1.4176 | 0.1367 | 0.1830 | 0.7079 | nan | 0.7035 | 0.9303 | 0.0 | 0.0255 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.8694 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8848 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9287 | 0.4230 | 0.9065 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4233 | 0.7534 | 0.0 | 0.0255 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.6246 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5331 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7049 | 0.3427 | 0.8312 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.4506 | 0.62 | 250 | 1.4011 | 0.1350 | 0.1827 | 0.7079 | nan | 0.6936 | 0.9362 | 0.0 | 0.0364 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.8955 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8612 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9299 | 0.3720 | 0.9394 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4251 | 0.7533 | 0.0 | 0.0363 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.5961 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5472 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6990 | 0.3026 | 0.8250 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.3095 | 0.69 | 275 | 1.4039 | 0.1398 | 0.1861 | 0.7134 | nan | 0.6873 | 0.9430 | 0.0 | 0.0260 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.8807 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8960 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9156 | 0.5055 | 0.9137 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4187 | 0.7531 | 0.0 | 0.0260 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.6172 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5457 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7261 | 0.4143 | 0.8343 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.4218 | 0.75 | 300 | 1.3735 | 0.1370 | 0.1856 | 0.7082 | nan | 0.7701 | 0.8980 | 0.0 | 0.0524 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.8776 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8762 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9444 | 0.4052 | 0.9279 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4267 | 0.7668 | 0.0 | 0.0522 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.6148 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5481 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6931 | 0.3263 | 0.8172 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.3986 | 0.81 | 325 | 1.3224 | 0.1451 | 0.1917 | 0.7223 | nan | 0.7354 | 0.9296 | 0.0 | 0.1261 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.8747 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8946 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9269 | 0.5292 | 0.9261 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4389 | 0.7700 | 0.0 | 0.1253 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.6395 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5541 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7200 | 0.4083 | 0.8409 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.3335 | 0.88 | 350 | 1.2909 | 0.1454 | 0.1921 | 0.7230 | nan | 0.7157 | 0.9388 | 0.0 | 0.1024 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.8896 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8932 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9207 | 0.5667 | 0.9276 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4347 | 0.7657 | 0.0 | 0.1020 | 0.0003 | nan | 0.0 | 0.0 | 0.0 | 0.6232 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5533 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7300 | 0.4607 | 0.8376 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.6376 | 0.94 | 375 | 1.3109 | 0.1476 | 0.1964 | 0.7253 | nan | 0.7617 | 0.9215 | 0.0 | 0.1225 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.9008 | 0.0 | 0.0 | nan | 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 | 0.9118 | 0.6529 | 0.9335 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4357 | 0.7750 | 0.0 | 0.1216 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.6139 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5572 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7387 | 0.5011 | 0.8333 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.4354 | 1.0 | 400 | 1.3483 | 0.1474 | 0.1958 | 0.7227 | nan | 0.7892 | 0.9075 | 0.0 | 0.1029 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.8749 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8943 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9118 | 0.6546 | 0.9352 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4282 | 0.7768 | 0.0 | 0.1021 | 0.0001 | nan | 0.0 | 0.0 | 0.0 | 0.6372 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5530 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7392 | 0.5009 | 0.8328 | 0.0 | 0.0 | 0.0 | 0.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.35.2
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+ - Pytorch 2.1.0+cu118
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+ - Datasets 2.15.0
147
+ - Tokenizers 0.15.0
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