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Akiteru
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my-fine-tuned-model

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  1. README.md +152 -0
  2. config.json +144 -0
  3. model.safetensors +3 -0
  4. preprocessor_config.json +23 -0
  5. runs/Jan21_05-14-20_jupyter-demo08/events.out.tfevents.1737436491.jupyter-demo08.501.0 +3 -0
  6. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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+ license: other
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+ base_model: nvidia/segformer-b1-finetuned-ade-512-512
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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: my-fine-tuned-model
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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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+ # my-fine-tuned-model
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+
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+ This model is a fine-tuned version of [nvidia/segformer-b1-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b1-finetuned-ade-512-512) on the segments/sidewalk-semantic dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.1999
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+ - Mean Iou: 0.1706
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+ - Mean Accuracy: 0.2116
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+ - Overall Accuracy: 0.7740
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Flat-road: 0.8915
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+ - Accuracy Flat-sidewalk: 0.9438
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+ - Accuracy Flat-crosswalk: 0.0
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+ - Accuracy Flat-cyclinglane: 0.5087
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+ - Accuracy Flat-parkingdriveway: 0.0048
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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: 0.8715
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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: 0.0
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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: 0.9030
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+ - Accuracy Construction-door: 0.0
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+ - Accuracy Construction-wall: 0.0009
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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: 0.9444
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+ - Accuracy Nature-terrain: 0.7861
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+ - Accuracy Sky: 0.9161
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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: 0.5823
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+ - Iou Flat-sidewalk: 0.8174
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+ - Iou Flat-crosswalk: 0.0
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+ - Iou Flat-cyclinglane: 0.4884
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+ - Iou Flat-parkingdriveway: 0.0048
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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: 0.6619
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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: 0.0
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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: 0.5862
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+ - Iou Construction-door: 0.0
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+ - Iou Construction-wall: 0.0009
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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: 0.7592
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+ - Iou Nature-terrain: 0.7159
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+ - Iou Sky: 0.8430
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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: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: linear
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+ - num_epochs: 2
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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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+ | 3.0054 | 0.1 | 20 | 2.8502 | 0.0610 | 0.1050 | 0.5551 | nan | 0.5630 | 0.9504 | 0.0190 | 0.0010 | 0.0022 | nan | 0.0020 | 0.0004 | 0.1899 | 0.0446 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0014 | 0.0 | 0.0 | 0.1945 | 0.0765 | 0.0374 | 0.0101 | 0.0 | nan | 0.0174 | 0.0025 | 0.0 | 0.0 | 0.9743 | 0.0000 | 0.2628 | 0.0 | 0.0081 | 0.0011 | 0.0 | 0.0 | 0.3847 | 0.7107 | 0.0132 | 0.0007 | 0.0022 | 0.0 | 0.0015 | 0.0004 | 0.0425 | 0.0434 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0014 | 0.0 | 0.0 | 0.1626 | 0.0084 | 0.0214 | 0.0082 | 0.0 | 0.0 | 0.0130 | 0.0021 | 0.0 | 0.0 | 0.4534 | 0.0000 | 0.2590 | 0.0 | 0.0056 | 0.0010 | 0.0 |
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+ | 2.9407 | 0.2 | 40 | 2.3892 | 0.0977 | 0.1428 | 0.6529 | nan | 0.7944 | 0.9095 | 0.0015 | 0.0004 | 0.0015 | nan | 0.0000 | 0.0 | 0.0029 | 0.4578 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0004 | 0.0 | 0.0 | 0.6666 | 0.0 | 0.0090 | 0.0 | 0.0 | nan | 0.0 | 0.0003 | 0.0 | 0.0 | 0.9809 | 0.0001 | 0.7443 | 0.0 | 0.0002 | 0.0008 | 0.0 | 0.0 | 0.4524 | 0.7526 | 0.0013 | 0.0003 | 0.0015 | 0.0 | 0.0000 | 0.0 | 0.0029 | 0.4135 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0004 | 0.0 | 0.0 | 0.4316 | 0.0 | 0.0085 | 0.0 | 0.0 | nan | 0.0 | 0.0003 | 0.0 | 0.0 | 0.5305 | 0.0001 | 0.7265 | 0.0 | 0.0002 | 0.0008 | 0.0 |
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+ | 2.548 | 0.3 | 60 | 2.1416 | 0.1163 | 0.1564 | 0.6823 | nan | 0.7335 | 0.9336 | 0.0004 | 0.0009 | 0.0021 | nan | 0.0 | 0.0 | 0.0 | 0.6875 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8169 | 0.0 | 0.0109 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9771 | 0.0554 | 0.7873 | 0.0 | 0.0 | 0.0002 | 0.0 | nan | 0.4575 | 0.7461 | 0.0004 | 0.0009 | 0.0021 | nan | 0.0 | 0.0 | 0.0 | 0.5870 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5082 | 0.0 | 0.0108 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5896 | 0.0550 | 0.7637 | 0.0 | 0.0 | 0.0002 | 0.0 |
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+ | 2.0998 | 0.4 | 80 | 1.9659 | 0.1286 | 0.1704 | 0.7058 | nan | 0.7787 | 0.9334 | 0.0000 | 0.0001 | 0.0012 | nan | 0.0 | 0.0 | 0.0 | 0.7274 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8594 | 0.0 | 0.0069 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9643 | 0.3080 | 0.8732 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.4742 | 0.7686 | 0.0000 | 0.0001 | 0.0012 | nan | 0.0 | 0.0 | 0.0 | 0.6040 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5211 | 0.0 | 0.0068 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6503 | 0.2984 | 0.7912 | 0.0 | 0.0 | 0.0000 | 0.0 |
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+ | 1.9886 | 0.5 | 100 | 1.8620 | 0.1292 | 0.1707 | 0.7068 | nan | 0.8207 | 0.9258 | 0.0 | 0.0000 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.6582 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8540 | 0.0 | 0.0017 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9666 | 0.3605 | 0.8740 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.4776 | 0.7811 | 0.0 | 0.0000 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.5764 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5022 | 0.0 | 0.0017 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6580 | 0.3531 | 0.7829 | 0.0 | 0.0 | 0.0000 | 0.0 |
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+ | 1.7382 | 0.6 | 120 | 1.7216 | 0.1316 | 0.1728 | 0.7127 | nan | 0.7315 | 0.9586 | 0.0 | 0.0034 | 0.0011 | nan | 0.0 | 0.0 | 0.0 | 0.7762 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8834 | 0.0 | 0.0023 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9507 | 0.3498 | 0.8714 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4817 | 0.7575 | 0.0 | 0.0034 | 0.0011 | nan | 0.0 | 0.0 | 0.0 | 0.6358 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5209 | 0.0 | 0.0023 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6836 | 0.3412 | 0.7841 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.796 | 0.7 | 140 | 1.6579 | 0.1395 | 0.1820 | 0.7278 | nan | 0.8098 | 0.9548 | 0.0 | 0.0015 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.8423 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8210 | 0.0 | 0.0019 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9476 | 0.5269 | 0.9178 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4949 | 0.7711 | 0.0 | 0.0015 | 0.0002 | nan | 0.0 | 0.0 | 0.0 | 0.6303 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5644 | 0.0 | 0.0019 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7109 | 0.5046 | 0.7848 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.7154 | 0.8 | 160 | 1.5859 | 0.1430 | 0.1852 | 0.7305 | nan | 0.8789 | 0.9173 | 0.0 | 0.0269 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.7923 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9148 | 0.0 | 0.0003 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9430 | 0.5814 | 0.8701 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5088 | 0.7927 | 0.0 | 0.0269 | 0.0000 | nan | 0.0 | 0.0 | 0.0 | 0.6391 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5247 | 0.0 | 0.0003 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7262 | 0.5517 | 0.8041 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.7287 | 0.9 | 180 | 1.4827 | 0.1507 | 0.1914 | 0.7471 | nan | 0.8611 | 0.9539 | 0.0 | 0.0168 | 0.0013 | nan | 0.0 | 0.0 | 0.0 | 0.8329 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8750 | 0.0 | 0.0009 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9494 | 0.7283 | 0.9041 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5268 | 0.7957 | 0.0 | 0.0168 | 0.0013 | nan | 0.0 | 0.0 | 0.0 | 0.6465 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5805 | 0.0 | 0.0009 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7341 | 0.6741 | 0.8460 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.7761 | 1.0 | 200 | 1.4371 | 0.1462 | 0.1888 | 0.7395 | nan | 0.8871 | 0.9276 | 0.0 | 0.1441 | 0.0013 | nan | 0.0 | 0.0 | 0.0 | 0.8294 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8938 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9572 | 0.4822 | 0.9179 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5213 | 0.8143 | 0.0 | 0.1435 | 0.0013 | nan | 0.0 | 0.0 | 0.0 | 0.6415 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5569 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7003 | 0.4663 | 0.8340 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.3667 | 1.1 | 220 | 1.3822 | 0.1581 | 0.1972 | 0.7497 | nan | 0.7334 | 0.9680 | 0.0 | 0.3107 | 0.0039 | nan | 0.0 | 0.0 | 0.0 | 0.8475 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9153 | 0.0 | 0.0003 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9467 | 0.6836 | 0.9024 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5388 | 0.7608 | 0.0 | 0.3052 | 0.0039 | nan | 0.0 | 0.0 | 0.0 | 0.6523 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5660 | 0.0 | 0.0003 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7450 | 0.6425 | 0.8458 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.4033 | 1.2 | 240 | 1.3556 | 0.1607 | 0.2017 | 0.7601 | nan | 0.8631 | 0.9523 | 0.0 | 0.2805 | 0.0028 | nan | 0.0 | 0.0 | 0.0 | 0.8582 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8912 | 0.0 | 0.0003 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9393 | 0.7424 | 0.9249 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5498 | 0.8023 | 0.0 | 0.2776 | 0.0028 | nan | 0.0 | 0.0 | 0.0 | 0.6621 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5761 | 0.0 | 0.0003 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7566 | 0.6899 | 0.8241 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.2975 | 1.3 | 260 | 1.2957 | 0.1662 | 0.2062 | 0.7674 | nan | 0.8769 | 0.9483 | 0.0 | 0.4108 | 0.0040 | nan | 0.0 | 0.0 | 0.0 | 0.8624 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8958 | 0.0 | 0.0017 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9524 | 0.7475 | 0.8975 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5783 | 0.8042 | 0.0 | 0.3990 | 0.0040 | nan | 0.0 | 0.0 | 0.0 | 0.6652 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5960 | 0.0 | 0.0017 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7384 | 0.6849 | 0.8452 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.113 | 1.4 | 280 | 1.2796 | 0.1656 | 0.2069 | 0.7657 | nan | 0.8637 | 0.9449 | 0.0 | 0.4093 | 0.0061 | nan | 0.0 | 0.0 | 0.0 | 0.8670 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9138 | 0.0 | 0.0012 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9372 | 0.7764 | 0.9027 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5655 | 0.8067 | 0.0 | 0.3983 | 0.0061 | nan | 0.0 | 0.0 | 0.0 | 0.6548 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5752 | 0.0 | 0.0012 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7586 | 0.7044 | 0.8286 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.7147 | 1.5 | 300 | 1.2507 | 0.1668 | 0.2087 | 0.7667 | nan | 0.8655 | 0.9449 | 0.0 | 0.4471 | 0.0048 | nan | 0.0 | 0.0 | 0.0 | 0.8668 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8873 | 0.0 | 0.0014 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9354 | 0.8027 | 0.9226 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5655 | 0.8025 | 0.0 | 0.4325 | 0.0048 | nan | 0.0 | 0.0 | 0.0 | 0.6591 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5780 | 0.0 | 0.0014 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7601 | 0.7135 | 0.8216 | 0.0 | 0.0 | 0.0 | 0.0 |
140
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+
146
+
147
+ ### Framework versions
148
+
149
+ - Transformers 4.48.0
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+ - Pytorch 2.1.1+cu118
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+ - Datasets 3.2.0
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+ - Tokenizers 0.21.0
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