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segformer-b0-finetuned-segments-sidewalk-2

Image Segmentation
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Safetensors
segformer
vision
Generated from Trainer
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  1. README.md +138 -198
  2. config.json +144 -0
  3. model.safetensors +3 -0
  4. runs/Sep08_16-18-23_ebf4b5ae847d/events.out.tfevents.1725779943.ebf4b5ae847d +3 -0
  5. runs/Sep08_16-18-23_ebf4b5ae847d/events.out.tfevents.1725780392.ebf4b5ae847d +3 -0
  6. runs/Sep08_16-33-46_ebf4b5ae847d/events.out.tfevents.1725780828.ebf4b5ae847d +3 -0
  7. runs/Sep08_16-38-23_ebf4b5ae847d/events.out.tfevents.1725781107.ebf4b5ae847d +3 -0
  8. runs/Sep08_16-44-49_ebf4b5ae847d/events.out.tfevents.1725781491.ebf4b5ae847d +3 -0
  9. runs/Sep08_16-49-46_ebf4b5ae847d/events.out.tfevents.1725781790.ebf4b5ae847d +3 -0
  10. runs/Sep08_17-05-05_ebf4b5ae847d/events.out.tfevents.1725782708.ebf4b5ae847d +3 -0
  11. runs/Sep08_18-05-32_ebf4b5ae847d/events.out.tfevents.1725786336.ebf4b5ae847d +3 -0
  12. runs/Sep09_11-55-18_81606da62db9/events.out.tfevents.1725850531.81606da62db9 +3 -0
  13. runs/Sep09_11-57-47_81606da62db9/events.out.tfevents.1725850670.81606da62db9 +3 -0
  14. training_args.bin +3 -0
README.md CHANGED
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  ---
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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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-
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- ## Model Details
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- ### Model Description
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- ## Uses
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- ### Downstream Use [optional]
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- ## Bias, Risks, and Limitations
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- ## Training Details
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- ### Training Data
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- ### Training Procedure
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- #### Preprocessing [optional]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- ## Evaluation
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- ### Testing Data, Factors & Metrics
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- #### Factors
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- ## Technical Specifications [optional]
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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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+
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+ # segformer-b0-finetuned-segments-sidewalk-2
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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: 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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+
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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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+
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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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+
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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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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:|
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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 |
131
+ | 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 |
132
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134
+
135
+
136
+ ### Framework versions
137
+
138
+ - Transformers 4.38.1
139
+ - Pytorch 2.2.1+cu118
140
+ - Datasets 2.17.1
141
+ - Tokenizers 0.15.2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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