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segformer-b0-finetuned-segments-sidewalk-oct-22

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  1. README.md +168 -195
  2. config.json +144 -0
  3. model.safetensors +3 -0
  4. runs/Dec05_02-26-43_1e955daa0c6f/events.out.tfevents.1733365690.1e955daa0c6f.282.0 +3 -0
  5. runs/Dec05_04-25-27_1e955daa0c6f/events.out.tfevents.1733372747.1e955daa0c6f.29407.0 +3 -0
  6. 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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- ## 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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- - **Developed by:** [More Information Needed]
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- ## Uses
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- ### Direct Use
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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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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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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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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- ## Evaluation
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- #### Factors
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- #### Metrics
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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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- ### Model Architecture and Objective
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  ---
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  library_name: transformers
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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-oct-22
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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-oct-22
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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: 0.7351
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+ - Mean Iou: 0.2097
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+ - Mean Accuracy: 0.2588
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+ - Overall Accuracy: 0.7962
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+ - Accuracy Unlabeled: nan
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+ - Accuracy Flat-road: 0.7781
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+ - Accuracy Flat-sidewalk: 0.9422
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+ - Accuracy Flat-crosswalk: 0.0
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+ - Accuracy Flat-cyclinglane: 0.6678
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+ - Accuracy Flat-parkingdriveway: 0.5103
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+ - Accuracy Flat-railtrack: nan
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+ - Accuracy Flat-curb: 0.3970
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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: 0.9361
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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: 0.9283
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+ - Accuracy Construction-door: 0.0
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+ - Accuracy Construction-wall: 0.2268
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+ - Accuracy Construction-fenceguardrail: 0.0000
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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: 0.0873
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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: 0.9230
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+ - Accuracy Nature-terrain: 0.9084
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+ - Accuracy Sky: 0.9461
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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: 0.0306
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+ - Accuracy Void-unclear: 0.0
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+ - Iou Unlabeled: nan
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+ - Iou Flat-road: 0.6262
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+ - Iou Flat-sidewalk: 0.8234
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+ - Iou Flat-crosswalk: 0.0
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+ - Iou Flat-cyclinglane: 0.5624
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+ - Iou Flat-parkingdriveway: 0.3620
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+ - Iou Flat-railtrack: nan
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+ - Iou Flat-curb: 0.3029
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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: 0.7060
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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: 0.6436
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+ - Iou Construction-door: 0.0
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+ - Iou Construction-wall: 0.1895
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+ - Iou Construction-fenceguardrail: 0.0000
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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: 0.0808
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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: 0.7906
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+ - Iou Nature-terrain: 0.7024
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+ - Iou Sky: 0.8933
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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: 0.0273
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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: 0.0002
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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: 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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+ | 2.6052 | 0.05 | 20 | 2.6583 | 0.0781 | 0.1236 | 0.5809 | nan | 0.1191 | 0.9803 | 0.0 | 0.0 | 0.0014 | nan | 0.0011 | 0.0 | 0.0 | 0.9544 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6683 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8576 | 0.0002 | 0.3741 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.1069 | 0.5811 | 0.0 | 0.0 | 0.0014 | nan | 0.0011 | 0.0 | 0.0 | 0.4003 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4146 | 0.0 | 0.0000 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6237 | 0.0002 | 0.3710 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.9158 | 0.1 | 40 | 1.8276 | 0.0852 | 0.1341 | 0.6115 | nan | 0.8028 | 0.8197 | 0.0 | 0.0 | 0.0043 | nan | 0.0013 | 0.0 | 0.0 | 0.8105 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8498 | 0.0 | 0.0002 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9232 | 0.0006 | 0.0783 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3391 | 0.6942 | 0.0 | 0.0 | 0.0042 | nan | 0.0013 | 0.0 | 0.0 | 0.5540 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.4445 | 0.0 | 0.0002 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6091 | 0.0006 | 0.0781 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.4713 | 0.15 | 60 | 1.5056 | 0.1027 | 0.1504 | 0.6575 | nan | 0.6471 | 0.9460 | 0.0 | 0.0 | 0.0031 | nan | 0.0043 | 0.0 | 0.0 | 0.9179 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8383 | 0.0 | 0.0002 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9344 | 0.0030 | 0.5194 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3964 | 0.7171 | 0.0 | 0.0 | 0.0031 | nan | 0.0043 | 0.0 | 0.0 | 0.5233 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5092 | 0.0 | 0.0002 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6168 | 0.0030 | 0.5139 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.1025 | 0.2 | 80 | 1.3972 | 0.1167 | 0.1598 | 0.6714 | nan | 0.6796 | 0.9507 | 0.0 | 0.0001 | 0.0002 | nan | 0.0017 | 0.0 | 0.0 | 0.7917 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8734 | 0.0 | 0.0090 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9286 | 0.0247 | 0.8540 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4167 | 0.7149 | 0.0 | 0.0001 | 0.0002 | nan | 0.0017 | 0.0 | 0.0 | 0.6355 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5041 | 0.0 | 0.0087 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6289 | 0.0244 | 0.7983 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.9869 | 0.25 | 100 | 1.3690 | 0.1082 | 0.1580 | 0.6565 | nan | 0.5658 | 0.9480 | 0.0 | 0.0005 | 0.0005 | nan | 0.0005 | 0.0 | 0.0 | 0.9066 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.7864 | 0.0 | 0.0055 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9378 | 0.0041 | 0.9016 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3963 | 0.6944 | 0.0 | 0.0005 | 0.0005 | nan | 0.0005 | 0.0 | 0.0 | 0.4568 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5445 | 0.0 | 0.0053 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.5857 | 0.0041 | 0.7738 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.126 | 0.3 | 120 | 1.2700 | 0.1288 | 0.1754 | 0.6867 | nan | 0.8098 | 0.8984 | 0.0 | 0.0093 | 0.0002 | nan | 0.0001 | 0.0 | 0.0 | 0.8207 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8592 | 0.0 | 0.0043 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9337 | 0.3991 | 0.8793 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4231 | 0.7335 | 0.0 | 0.0093 | 0.0002 | nan | 0.0001 | 0.0 | 0.0 | 0.6281 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5333 | 0.0 | 0.0042 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6647 | 0.3577 | 0.7679 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.3711 | 0.35 | 140 | 1.1517 | 0.1452 | 0.1931 | 0.7186 | nan | 0.7518 | 0.9370 | 0.0 | 0.2270 | 0.0011 | nan | 0.0001 | 0.0 | 0.0 | 0.9359 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8504 | 0.0 | 0.0036 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9066 | 0.6993 | 0.8660 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4932 | 0.7384 | 0.0 | 0.2135 | 0.0011 | nan | 0.0001 | 0.0 | 0.0 | 0.5199 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5779 | 0.0 | 0.0035 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7254 | 0.5490 | 0.8244 | 0.0 | 0.0 | 0.0 | 0.0 |
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+ | 1.3994 | 0.4 | 160 | 1.1185 | 0.1508 | 0.1970 | 0.7199 | nan | 0.8502 | 0.9062 | 0.0 | 0.2392 | 0.0004 | nan | 0.0001 | 0.0 | 0.0 | 0.8281 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8655 | 0.0 | 0.0017 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8850 | 0.7953 | 0.9333 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4463 | 0.7613 | 0.0 | 0.2293 | 0.0004 | nan | 0.0001 | 0.0 | 0.0 | 0.6460 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5373 | 0.0 | 0.0017 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7474 | 0.6127 | 0.8426 | 0.0 | 0.0 | 0.0 | 0.0 |
133
+ | 0.911 | 0.45 | 180 | 1.0558 | 0.1550 | 0.2026 | 0.7305 | nan | 0.7464 | 0.9479 | 0.0 | 0.4110 | 0.0128 | nan | 0.0013 | 0.0 | 0.0 | 0.9009 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9054 | 0.0 | 0.0008 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8071 | 0.8876 | 0.8631 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5120 | 0.7481 | 0.0 | 0.3866 | 0.0127 | nan | 0.0013 | 0.0 | 0.0 | 0.6344 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5574 | 0.0 | 0.0007 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7060 | 0.5653 | 0.8353 | 0.0 | 0.0 | 0.0 | 0.0 |
134
+ | 1.8468 | 0.5 | 200 | 1.0063 | 0.1573 | 0.2034 | 0.7369 | nan | 0.8325 | 0.9268 | 0.0 | 0.3866 | 0.0021 | nan | 0.0029 | 0.0 | 0.0 | 0.9098 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8590 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9134 | 0.7506 | 0.9239 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.4926 | 0.7629 | 0.0 | 0.3684 | 0.0021 | nan | 0.0029 | 0.0 | 0.0 | 0.6087 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5709 | 0.0 | 0.0001 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7512 | 0.6387 | 0.8367 | 0.0 | 0.0 | 0.0 | 0.0 |
135
+ | 0.8309 | 0.55 | 220 | 0.9882 | 0.1604 | 0.2073 | 0.7393 | nan | 0.7298 | 0.9406 | 0.0 | 0.3782 | 0.0239 | nan | 0.0704 | 0.0 | 0.0 | 0.9040 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8975 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9062 | 0.8872 | 0.8960 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5191 | 0.7437 | 0.0 | 0.3403 | 0.0235 | nan | 0.0654 | 0.0 | 0.0 | 0.6150 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5780 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7599 | 0.6547 | 0.8333 | 0.0 | 0.0 | 0.0 | 0.0 |
136
+ | 1.5283 | 0.6 | 240 | 0.9686 | 0.1657 | 0.2120 | 0.7435 | nan | 0.8319 | 0.9088 | 0.0 | 0.5127 | 0.0254 | nan | 0.1306 | 0.0 | 0.0 | 0.8869 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8993 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9219 | 0.7303 | 0.9347 | 0.0 | 0.0 | 0.0000 | 0.0 | nan | 0.5035 | 0.7652 | 0.0 | 0.4476 | 0.0250 | nan | 0.1151 | 0.0 | 0.0 | 0.6605 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5813 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7522 | 0.6158 | 0.8355 | 0.0 | 0.0 | 0.0000 | 0.0 |
137
+ | 0.5838 | 0.65 | 260 | 0.9858 | 0.1667 | 0.2164 | 0.7450 | nan | 0.7064 | 0.9452 | 0.0 | 0.4848 | 0.1076 | nan | 0.1731 | 0.0 | 0.0 | 0.9220 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8139 | 0.0 | 0.0006 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9085 | 0.9244 | 0.9371 | 0.0 | 0.0 | 0.0006 | 0.0 | nan | 0.5492 | 0.7671 | 0.0 | 0.4391 | 0.1003 | nan | 0.1451 | 0.0 | 0.0 | 0.6131 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5788 | 0.0 | 0.0006 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.6824 | 0.5950 | 0.8630 | 0.0 | 0.0 | 0.0006 | 0.0 |
138
+ | 1.0103 | 0.7 | 280 | 0.9187 | 0.1717 | 0.2174 | 0.7573 | nan | 0.7729 | 0.9468 | 0.0 | 0.5562 | 0.1364 | nan | 0.1327 | 0.0 | 0.0 | 0.9128 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8647 | 0.0 | 0.0024 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9379 | 0.7704 | 0.9241 | 0.0 | 0.0 | 0.0001 | 0.0 | nan | 0.5545 | 0.7857 | 0.0 | 0.4650 | 0.1235 | nan | 0.1163 | 0.0 | 0.0 | 0.6424 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5814 | 0.0 | 0.0024 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7353 | 0.6449 | 0.8444 | 0.0 | 0.0 | 0.0001 | 0.0 |
139
+ | 0.7643 | 0.75 | 300 | 0.9432 | 0.1678 | 0.2079 | 0.7494 | nan | 0.7337 | 0.9651 | 0.0 | 0.5139 | 0.0532 | nan | 0.0619 | 0.0 | 0.0 | 0.8188 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8693 | 0.0 | 0.0020 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9373 | 0.7835 | 0.9152 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5522 | 0.7514 | 0.0 | 0.4670 | 0.0507 | nan | 0.0576 | 0.0 | 0.0 | 0.6797 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5663 | 0.0 | 0.0019 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7301 | 0.6469 | 0.8644 | 0.0 | 0.0 | 0.0 | 0.0 |
140
+ | 0.6425 | 0.8 | 320 | 0.9611 | 0.1722 | 0.2218 | 0.7449 | nan | 0.6811 | 0.9292 | 0.0 | 0.5486 | 0.1368 | nan | 0.2891 | 0.0 | 0.0 | 0.8899 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9512 | 0.0 | 0.0013 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.8459 | 0.9144 | 0.9087 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.5319 | 0.7647 | 0.0 | 0.4224 | 0.1236 | nan | 0.2104 | 0.0 | 0.0 | 0.6475 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5370 | 0.0 | 0.0013 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7571 | 0.6566 | 0.8577 | 0.0 | 0.0 | 0.0 | 0.0 |
141
+ | 0.9292 | 0.85 | 340 | 0.8890 | 0.1759 | 0.2213 | 0.7594 | nan | 0.6721 | 0.9524 | 0.0 | 0.6827 | 0.1609 | nan | 0.1927 | 0.0 | 0.0 | 0.8767 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8994 | 0.0 | 0.0025 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.9567 | 0.7380 | 0.9474 | 0.0 | 0.0 | 0.0005 | 0.0 | nan | 0.5323 | 0.7863 | 0.0 | 0.4764 | 0.1441 | nan | 0.1562 | 0.0 | 0.0 | 0.6933 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5952 | 0.0 | 0.0025 | 0.0 | 0.0 | nan | 0.0 | 0.0 | 0.0 | 0.0 | 0.7359 | 0.6399 | 0.8669 | 0.0 | 0.0 | 0.0005 | 0.0 |
142
+ | 0.6201 | 0.9 | 360 | 0.8572 | 0.1871 | 0.2354 | 0.7712 | nan | 0.6941 | 0.9488 | 0.0 | 0.6405 | 0.4223 | nan | 0.2294 | 0.0 | 0.0 | 0.9290 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8868 | 0.0 | 0.0532 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.9245 | 0.8871 | 0.9148 | 0.0 | 0.0 | 0.0014 | 0.0 | nan | 0.5658 | 0.7930 | 0.0 | 0.5446 | 0.2794 | nan | 0.1875 | 0.0 | 0.0 | 0.6262 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6075 | 0.0 | 0.0508 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.7662 | 0.6993 | 0.8658 | 0.0 | 0.0 | 0.0014 | 0.0 |
143
+ | 1.0028 | 0.95 | 380 | 0.8642 | 0.1790 | 0.2248 | 0.7648 | nan | 0.7265 | 0.9510 | 0.0 | 0.6286 | 0.1598 | nan | 0.1719 | 0.0 | 0.0 | 0.9213 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9119 | 0.0 | 0.0156 | 0.0 | 0.0 | nan | 0.0 | 0.0007 | 0.0 | 0.0 | 0.9108 | 0.8756 | 0.9200 | 0.0 | 0.0 | 0.0011 | 0.0 | nan | 0.5445 | 0.7817 | 0.0 | 0.4995 | 0.1473 | nan | 0.1486 | 0.0 | 0.0 | 0.6385 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5978 | 0.0 | 0.0153 | 0.0 | 0.0 | nan | 0.0 | 0.0007 | 0.0 | 0.0 | 0.7795 | 0.6997 | 0.8726 | 0.0 | 0.0 | 0.0011 | 0.0 |
144
+ | 0.8125 | 1.0 | 400 | 0.8575 | 0.1836 | 0.2336 | 0.7707 | nan | 0.8044 | 0.9387 | 0.0 | 0.5612 | 0.2633 | nan | 0.2876 | 0.0 | 0.0 | 0.9481 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8694 | 0.0 | 0.0216 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.8947 | 0.9314 | 0.9469 | 0.0 | 0.0 | 0.0072 | 0.0 | nan | 0.5789 | 0.7979 | 0.0 | 0.4938 | 0.2217 | nan | 0.2231 | 0.0 | 0.0 | 0.6085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6185 | 0.0 | 0.0214 | 0.0 | 0.0 | nan | 0.0 | 0.0002 | 0.0 | 0.0 | 0.7762 | 0.6458 | 0.8823 | 0.0 | 0.0 | 0.0069 | 0.0 |
145
+ | 0.7077 | 1.05 | 420 | 0.8753 | 0.1832 | 0.2253 | 0.7673 | nan | 0.7668 | 0.9672 | 0.0 | 0.4280 | 0.2171 | nan | 0.2647 | 0.0 | 0.0 | 0.8745 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9391 | 0.0 | 0.0469 | 0.0 | 0.0 | nan | 0.0 | 0.0010 | 0.0 | 0.0 | 0.8847 | 0.8916 | 0.9224 | 0.0 | 0.0 | 0.0064 | 0.0 | nan | 0.5851 | 0.7829 | 0.0 | 0.4063 | 0.1772 | nan | 0.2167 | 0.0 | 0.0 | 0.6977 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5724 | 0.0 | 0.0444 | 0.0 | 0.0 | nan | 0.0 | 0.0010 | 0.0 | 0.0 | 0.7869 | 0.7071 | 0.8785 | 0.0 | 0.0 | 0.0060 | 0.0 |
146
+ | 0.5107 | 1.1 | 440 | 0.8497 | 0.1901 | 0.2344 | 0.7732 | nan | 0.7275 | 0.9648 | 0.0 | 0.6019 | 0.1918 | nan | 0.2115 | 0.0 | 0.0 | 0.8908 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8728 | 0.0 | 0.2648 | 0.0 | 0.0 | nan | 0.0 | 0.0023 | 0.0 | 0.0 | 0.9263 | 0.9003 | 0.9358 | 0.0 | 0.0 | 0.0106 | 0.0 | nan | 0.5929 | 0.7820 | 0.0 | 0.4854 | 0.1647 | nan | 0.1832 | 0.0 | 0.0 | 0.7128 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6123 | 0.0 | 0.1969 | 0.0 | 0.0 | nan | 0.0 | 0.0023 | 0.0 | 0.0 | 0.7653 | 0.6963 | 0.8804 | 0.0 | 0.0 | 0.0098 | 0.0 |
147
+ | 0.864 | 1.15 | 460 | 0.8285 | 0.1871 | 0.2330 | 0.7743 | nan | 0.8138 | 0.9395 | 0.0 | 0.6150 | 0.2174 | nan | 0.2799 | 0.0 | 0.0 | 0.9398 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8939 | 0.0 | 0.0380 | 0.0 | 0.0 | nan | 0.0 | 0.0071 | 0.0 | 0.0 | 0.9182 | 0.8546 | 0.9326 | 0.0 | 0.0 | 0.0072 | 0.0 | nan | 0.5785 | 0.7945 | 0.0 | 0.4986 | 0.1889 | nan | 0.2285 | 0.0 | 0.0 | 0.6345 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6203 | 0.0 | 0.0356 | 0.0 | 0.0 | nan | 0.0 | 0.0071 | 0.0 | 0.0 | 0.7858 | 0.7263 | 0.8831 | 0.0 | 0.0 | 0.0070 | 0.0 |
148
+ | 1.1038 | 1.2 | 480 | 0.8424 | 0.1934 | 0.2389 | 0.7756 | nan | 0.6686 | 0.9679 | 0.0 | 0.6309 | 0.3414 | nan | 0.3363 | 0.0 | 0.0 | 0.8914 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9260 | 0.0 | 0.0435 | 0.0 | 0.0 | nan | 0.0 | 0.0439 | 0.0 | 0.0 | 0.8998 | 0.9057 | 0.9432 | 0.0 | 0.0 | 0.0451 | 0.0 | nan | 0.5839 | 0.7944 | 0.0 | 0.5166 | 0.2481 | nan | 0.2274 | 0.0 | 0.0 | 0.7174 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.5978 | 0.0 | 0.0388 | 0.0 | 0.0 | nan | 0.0 | 0.0429 | 0.0 | 0.0 | 0.7874 | 0.7051 | 0.8895 | 0.0 | 0.0 | 0.0383 | 0.0 |
149
+ | 0.7316 | 1.25 | 500 | 0.8044 | 0.2013 | 0.2515 | 0.7831 | nan | 0.7636 | 0.9435 | 0.0 | 0.5808 | 0.4689 | nan | 0.4001 | 0.0 | 0.0 | 0.9282 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8854 | 0.0 | 0.2230 | 0.0 | 0.0 | nan | 0.0 | 0.0374 | 0.0 | 0.0 | 0.9060 | 0.9175 | 0.9477 | 0.0 | 0.0 | 0.0447 | 0.0 | nan | 0.5973 | 0.8132 | 0.0 | 0.5180 | 0.3150 | nan | 0.2651 | 0.0 | 0.0 | 0.6912 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6288 | 0.0 | 0.1796 | 0.0 | 0.0 | nan | 0.0 | 0.0365 | 0.0 | 0.0 | 0.7830 | 0.6928 | 0.8835 | 0.0 | 0.0 | 0.0364 | 0.0 |
150
+ | 0.8354 | 1.3 | 520 | 0.8133 | 0.1914 | 0.2358 | 0.7725 | nan | 0.8282 | 0.9275 | 0.0 | 0.4710 | 0.3078 | nan | 0.2966 | 0.0 | 0.0 | 0.9124 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9318 | 0.0 | 0.1146 | 0.0 | 0.0 | nan | 0.0 | 0.0343 | 0.0 | 0.0 | 0.9260 | 0.8574 | 0.9263 | 0.0 | 0.0 | 0.0108 | 0.0 | nan | 0.5677 | 0.7906 | 0.0 | 0.4449 | 0.2616 | nan | 0.2338 | 0.0 | 0.0 | 0.7037 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6056 | 0.0 | 0.1018 | 0.0 | 0.0 | nan | 0.0 | 0.0340 | 0.0 | 0.0 | 0.7789 | 0.7060 | 0.8866 | 0.0 | 0.0 | 0.0101 | 0.0 |
151
+ | 0.393 | 1.35 | 540 | 0.7798 | 0.2001 | 0.2497 | 0.7829 | nan | 0.7838 | 0.9291 | 0.0 | 0.6871 | 0.3828 | nan | 0.3642 | 0.0 | 0.0 | 0.9158 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8658 | 0.0 | 0.2278 | 0.0 | 0.0 | nan | 0.0 | 0.0377 | 0.0 | 0.0 | 0.9394 | 0.8976 | 0.9466 | 0.0 | 0.0 | 0.0123 | 0.0 | nan | 0.6044 | 0.8051 | 0.0 | 0.5457 | 0.2971 | nan | 0.2586 | 0.0 | 0.0 | 0.6959 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6314 | 0.0 | 0.1836 | 0.0 | 0.0 | nan | 0.0 | 0.0368 | 0.0 | 0.0 | 0.7648 | 0.6827 | 0.8847 | 0.0 | 0.0 | 0.0115 | 0.0 |
152
+ | 0.8005 | 1.4 | 560 | 0.7905 | 0.1999 | 0.2438 | 0.7834 | nan | 0.7293 | 0.9711 | 0.0 | 0.6267 | 0.2994 | nan | 0.2955 | 0.0 | 0.0 | 0.9193 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.9042 | 0.0 | 0.3408 | 0.0 | 0.0 | nan | 0.0 | 0.0460 | 0.0 | 0.0 | 0.9256 | 0.8096 | 0.9264 | 0.0 | 0.0 | 0.0067 | 0.0 | nan | 0.6192 | 0.7916 | 0.0 | 0.5595 | 0.2382 | nan | 0.2245 | 0.0 | 0.0 | 0.6994 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6221 | 0.0 | 0.2395 | 0.0 | 0.0 | nan | 0.0 | 0.0452 | 0.0 | 0.0 | 0.7818 | 0.6788 | 0.8901 | 0.0 | 0.0 | 0.0065 | 0.0 |
153
+ | 1.1368 | 1.45 | 580 | 0.8022 | 0.1974 | 0.2481 | 0.7734 | nan | 0.8556 | 0.8998 | 0.0 | 0.5845 | 0.3805 | nan | 0.2213 | 0.0 | 0.0 | 0.9201 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8873 | 0.0 | 0.3416 | 0.0 | 0.0 | nan | 0.0 | 0.0725 | 0.0 | 0.0 | 0.9076 | 0.9167 | 0.9397 | 0.0 | 0.0 | 0.0117 | 0.0 | nan | 0.5673 | 0.7996 | 0.0 | 0.5118 | 0.2996 | nan | 0.1588 | 0.0 | 0.0 | 0.6774 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6371 | 0.0 | 0.2584 | 0.0 | 0.0 | nan | 0.0 | 0.0693 | 0.0 | 0.0 | 0.7568 | 0.6834 | 0.8876 | 0.0 | 0.0 | 0.0112 | 0.0 |
154
+ | 0.6787 | 1.5 | 600 | 0.7612 | 0.2084 | 0.2588 | 0.7929 | nan | 0.7932 | 0.9457 | 0.0 | 0.6633 | 0.4719 | nan | 0.3431 | 0.0 | 0.0 | 0.9169 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8916 | 0.0 | 0.3567 | 0.0 | 0.0 | nan | 0.0 | 0.0888 | 0.0 | 0.0 | 0.9000 | 0.9375 | 0.9516 | 0.0 | 0.0 | 0.0214 | 0.0 | nan | 0.6295 | 0.8204 | 0.0 | 0.5823 | 0.3363 | nan | 0.2593 | 0.0 | 0.0 | 0.7102 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6424 | 0.0 | 0.2590 | 0.0 | 0.0 | nan | 0.0 | 0.0830 | 0.0 | 0.0 | 0.7745 | 0.6670 | 0.8865 | 0.0 | 0.0 | 0.0198 | 0.0 |
155
+ | 1.1082 | 1.55 | 620 | 0.7512 | 0.2110 | 0.2590 | 0.7960 | nan | 0.7743 | 0.9544 | 0.0 | 0.6595 | 0.5236 | nan | 0.3373 | 0.0 | 0.0 | 0.9282 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.8915 | 0.0 | 0.3487 | 0.0 | 0.0 | nan | 0.0 | 0.0861 | 0.0 | 0.0 | 0.9251 | 0.8795 | 0.9459 | 0.0 | 0.0 | 0.0328 | 0.0 | nan | 0.6434 | 0.8168 | 0.0 | 0.5709 | 0.3471 | nan | 0.2607 | 0.0 | 0.0 | 0.7052 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.6444 | 0.0 | 0.2489 | 0.0 | 0.0 | nan | 0.0 | 0.0802 | 0.0 | 0.0 | 0.7869 | 0.7279 | 0.8912 | 0.0 | 0.0 | 0.0287 | 0.0 |
156
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165
+
166
+
167
+ ### Framework versions
168
+
169
+ - Transformers 4.46.2
170
+ - Pytorch 2.5.1+cu121
171
+ - Datasets 3.1.0
172
+ - Tokenizers 0.20.3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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