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custom-object-masking_v4-2

Image Segmentation
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segformer
vision
Generated from Trainer
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  4. 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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  ---
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- # Model Card for Model ID
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-
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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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-
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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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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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- ### Downstream Use [optional]
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- ### Out-of-Scope Use
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- ## Bias, Risks, and Limitations
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- ### Recommendations
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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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- [More Information Needed]
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-
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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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- ## Environmental Impact
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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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- - **Hardware Type:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ## Glossary [optional]
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- ## More Information [optional]
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1
  ---
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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: custom-object-masking_v4-2
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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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+ # custom-object-masking_v4-2
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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 sungile/custom-object-masking_v4-2 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.1269
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+ - Mean Iou: 0.3975
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+ - Mean Accuracy: 0.7950
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+ - Overall Accuracy: 0.7950
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+ - Accuracy Unknown: nan
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+ - Accuracy Background: 0.7950
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+ - Accuracy Object: nan
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+ - Iou Unknown: 0.0
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+ - Iou Background: 0.7950
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+ - Iou Object: nan
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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: 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: 13
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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 Unknown | Accuracy Background | Accuracy Object | Iou Unknown | Iou Background | Iou Object |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:-------------------:|:---------------:|:-----------:|:--------------:|:----------:|
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+ | 0.8127 | 0.1 | 20 | 0.9327 | 0.2754 | 0.8263 | 0.8263 | nan | 0.8263 | nan | 0.0 | 0.8263 | 0.0 |
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+ | 0.6503 | 0.2 | 40 | 0.6689 | 0.2613 | 0.7839 | 0.7839 | nan | 0.7839 | nan | 0.0 | 0.7839 | 0.0 |
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+ | 0.6507 | 0.3 | 60 | 0.4836 | 0.1995 | 0.5986 | 0.5986 | nan | 0.5986 | nan | 0.0 | 0.5986 | 0.0 |
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+ | 0.4163 | 0.4 | 80 | 0.4203 | 0.3465 | 0.6930 | 0.6930 | nan | 0.6930 | nan | 0.0 | 0.6930 | nan |
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+ | 0.3638 | 0.5 | 100 | 0.3720 | 0.3254 | 0.6509 | 0.6509 | nan | 0.6509 | nan | 0.0 | 0.6509 | nan |
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+ | 0.2634 | 0.6 | 120 | 0.4453 | 0.4028 | 0.8057 | 0.8057 | nan | 0.8057 | nan | 0.0 | 0.8057 | nan |
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+ | 0.3585 | 0.7 | 140 | 0.3352 | 0.2434 | 0.4867 | 0.4867 | nan | 0.4867 | nan | 0.0 | 0.4867 | nan |
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+ | 0.2618 | 0.8 | 160 | 0.3338 | 0.3888 | 0.7776 | 0.7776 | nan | 0.7776 | nan | 0.0 | 0.7776 | nan |
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+ | 0.3289 | 0.9 | 180 | 0.2733 | 0.3065 | 0.6130 | 0.6130 | nan | 0.6130 | nan | 0.0 | 0.6130 | nan |
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+ | 0.3336 | 1.0 | 200 | 0.2792 | 0.3764 | 0.7527 | 0.7527 | nan | 0.7527 | nan | 0.0 | 0.7527 | nan |
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+ | 0.2173 | 1.1 | 220 | 0.2326 | 0.3132 | 0.6264 | 0.6264 | nan | 0.6264 | nan | 0.0 | 0.6264 | nan |
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+ | 0.1413 | 1.2 | 240 | 0.2333 | 0.2246 | 0.4491 | 0.4491 | nan | 0.4491 | nan | 0.0 | 0.4491 | nan |
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+ | 0.1734 | 1.3 | 260 | 0.2404 | 0.3939 | 0.7879 | 0.7879 | nan | 0.7879 | nan | 0.0 | 0.7879 | nan |
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+ | 0.2347 | 1.4 | 280 | 0.2115 | 0.3504 | 0.7007 | 0.7007 | nan | 0.7007 | nan | 0.0 | 0.7007 | nan |
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+ | 0.1318 | 1.5 | 300 | 0.1818 | 0.3459 | 0.6918 | 0.6918 | nan | 0.6918 | nan | 0.0 | 0.6918 | nan |
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+ | 0.1345 | 1.6 | 320 | 0.1732 | 0.3340 | 0.6681 | 0.6681 | nan | 0.6681 | nan | 0.0 | 0.6681 | nan |
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+ | 0.1137 | 1.7 | 340 | 0.1792 | 0.3690 | 0.7380 | 0.7380 | nan | 0.7380 | nan | 0.0 | 0.7380 | nan |
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+ | 0.2012 | 1.8 | 360 | 0.1758 | 0.3627 | 0.7255 | 0.7255 | nan | 0.7255 | nan | 0.0 | 0.7255 | nan |
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+ | 0.1846 | 1.9 | 380 | 0.1661 | 0.3545 | 0.7091 | 0.7091 | nan | 0.7091 | nan | 0.0 | 0.7091 | nan |
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+ | 0.2975 | 2.0 | 400 | 0.1639 | 0.3601 | 0.7201 | 0.7201 | nan | 0.7201 | nan | 0.0 | 0.7201 | nan |
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+ | 0.1781 | 2.1 | 420 | 0.1570 | 0.3175 | 0.6350 | 0.6350 | nan | 0.6350 | nan | 0.0 | 0.6350 | nan |
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+ | 0.0883 | 2.2 | 440 | 0.1468 | 0.3439 | 0.6878 | 0.6878 | nan | 0.6878 | nan | 0.0 | 0.6878 | nan |
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+ | 0.1184 | 2.3 | 460 | 0.1466 | 0.3646 | 0.7293 | 0.7293 | nan | 0.7293 | nan | 0.0 | 0.7293 | nan |
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+ | 0.1247 | 2.4 | 480 | 0.1476 | 0.3527 | 0.7054 | 0.7054 | nan | 0.7054 | nan | 0.0 | 0.7054 | nan |
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+ | 0.1346 | 2.5 | 500 | 0.1912 | 0.4292 | 0.8585 | 0.8585 | nan | 0.8585 | nan | 0.0 | 0.8585 | nan |
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+ | 0.0743 | 2.6 | 520 | 0.1441 | 0.3591 | 0.7183 | 0.7183 | nan | 0.7183 | nan | 0.0 | 0.7183 | nan |
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+ | 0.2979 | 2.7 | 540 | 0.1396 | 0.3757 | 0.7515 | 0.7515 | nan | 0.7515 | nan | 0.0 | 0.7515 | nan |
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+ | 0.2531 | 2.8 | 560 | 0.1382 | 0.3805 | 0.7609 | 0.7609 | nan | 0.7609 | nan | 0.0 | 0.7609 | nan |
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+ | 0.2609 | 2.9 | 580 | 0.1381 | 0.4094 | 0.8188 | 0.8188 | nan | 0.8188 | nan | 0.0 | 0.8188 | nan |
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+ | 0.0654 | 3.0 | 600 | 0.1386 | 0.3249 | 0.6498 | 0.6498 | nan | 0.6498 | nan | 0.0 | 0.6498 | nan |
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+ | 0.1333 | 3.1 | 620 | 0.1229 | 0.4029 | 0.8057 | 0.8057 | nan | 0.8057 | nan | 0.0 | 0.8057 | nan |
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+ | 0.0603 | 3.2 | 640 | 0.1512 | 0.2959 | 0.5918 | 0.5918 | nan | 0.5918 | nan | 0.0 | 0.5918 | nan |
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+ | 0.1016 | 3.3 | 660 | 0.1285 | 0.3670 | 0.7340 | 0.7340 | nan | 0.7340 | nan | 0.0 | 0.7340 | nan |
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+ | 0.1707 | 3.4 | 680 | 0.1220 | 0.4006 | 0.8012 | 0.8012 | nan | 0.8012 | nan | 0.0 | 0.8012 | nan |
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+ | 0.0486 | 3.5 | 700 | 0.1255 | 0.3517 | 0.7033 | 0.7033 | nan | 0.7033 | nan | 0.0 | 0.7033 | nan |
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+ | 0.057 | 3.6 | 720 | 0.1247 | 0.3955 | 0.7911 | 0.7911 | nan | 0.7911 | nan | 0.0 | 0.7911 | nan |
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+ | 0.4151 | 3.7 | 740 | 0.1319 | 0.3524 | 0.7048 | 0.7048 | nan | 0.7048 | nan | 0.0 | 0.7048 | nan |
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+ | 0.0938 | 3.8 | 760 | 0.1229 | 0.3919 | 0.7838 | 0.7838 | nan | 0.7838 | nan | 0.0 | 0.7838 | nan |
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+ | 0.1164 | 3.9 | 780 | 0.1197 | 0.4025 | 0.8051 | 0.8051 | nan | 0.8051 | nan | 0.0 | 0.8051 | nan |
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+ | 0.1879 | 4.0 | 800 | 0.1329 | 0.3305 | 0.6611 | 0.6611 | nan | 0.6611 | nan | 0.0 | 0.6611 | nan |
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+ | 0.1684 | 4.1 | 820 | 0.1201 | 0.4054 | 0.8108 | 0.8108 | nan | 0.8108 | nan | 0.0 | 0.8108 | nan |
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+ | 0.0384 | 4.2 | 840 | 0.1167 | 0.3843 | 0.7686 | 0.7686 | nan | 0.7686 | nan | 0.0 | 0.7686 | nan |
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+ | 0.1622 | 4.3 | 860 | 0.1122 | 0.3936 | 0.7872 | 0.7872 | nan | 0.7872 | nan | 0.0 | 0.7872 | nan |
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+ | 0.0381 | 4.4 | 880 | 0.1222 | 0.3630 | 0.7260 | 0.7260 | nan | 0.7260 | nan | 0.0 | 0.7260 | nan |
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+ | 0.036 | 4.5 | 900 | 0.1242 | 0.3969 | 0.7938 | 0.7938 | nan | 0.7938 | nan | 0.0 | 0.7938 | nan |
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+ | 0.0522 | 4.6 | 920 | 0.1245 | 0.3686 | 0.7372 | 0.7372 | nan | 0.7372 | nan | 0.0 | 0.7372 | nan |
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+ | 0.0292 | 4.7 | 940 | 0.1182 | 0.3714 | 0.7427 | 0.7427 | nan | 0.7427 | nan | 0.0 | 0.7427 | nan |
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+ | 0.0863 | 4.8 | 960 | 0.1148 | 0.4195 | 0.8390 | 0.8390 | nan | 0.8390 | nan | 0.0 | 0.8390 | nan |
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+ | 0.0869 | 4.9 | 980 | 0.1230 | 0.3929 | 0.7857 | 0.7857 | nan | 0.7857 | nan | 0.0 | 0.7857 | nan |
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+ | 0.04 | 5.0 | 1000 | 0.1235 | 0.3673 | 0.7346 | 0.7346 | nan | 0.7346 | nan | 0.0 | 0.7346 | nan |
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+ | 0.0597 | 5.1 | 1020 | 0.1172 | 0.3924 | 0.7848 | 0.7848 | nan | 0.7848 | nan | 0.0 | 0.7848 | nan |
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+ | 0.076 | 5.2 | 1040 | 0.1187 | 0.3828 | 0.7656 | 0.7656 | nan | 0.7656 | nan | 0.0 | 0.7656 | nan |
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+ | 0.0401 | 5.3 | 1060 | 0.1205 | 0.3860 | 0.7719 | 0.7719 | nan | 0.7719 | nan | 0.0 | 0.7719 | nan |
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+ | 0.0258 | 5.4 | 1080 | 0.1190 | 0.3840 | 0.7679 | 0.7679 | nan | 0.7679 | nan | 0.0 | 0.7679 | nan |
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+ | 0.0433 | 5.5 | 1100 | 0.1130 | 0.3986 | 0.7971 | 0.7971 | nan | 0.7971 | nan | 0.0 | 0.7971 | nan |
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+ | 0.0826 | 5.6 | 1120 | 0.1164 | 0.4074 | 0.8148 | 0.8148 | nan | 0.8148 | nan | 0.0 | 0.8148 | nan |
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+ | 0.0368 | 5.7 | 1140 | 0.1158 | 0.3834 | 0.7668 | 0.7668 | nan | 0.7668 | nan | 0.0 | 0.7668 | nan |
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+ | 0.0255 | 5.8 | 1160 | 0.1139 | 0.3842 | 0.7683 | 0.7683 | nan | 0.7683 | nan | 0.0 | 0.7683 | nan |
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+ | 0.1148 | 5.9 | 1180 | 0.1149 | 0.3806 | 0.7612 | 0.7612 | nan | 0.7612 | nan | 0.0 | 0.7612 | nan |
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+ | 0.0189 | 6.0 | 1200 | 0.1148 | 0.3997 | 0.7994 | 0.7994 | nan | 0.7994 | nan | 0.0 | 0.7994 | nan |
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+ | 0.0233 | 6.1 | 1220 | 0.1179 | 0.3747 | 0.7494 | 0.7494 | nan | 0.7494 | nan | 0.0 | 0.7494 | nan |
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+ | 0.0208 | 6.2 | 1240 | 0.1183 | 0.4066 | 0.8132 | 0.8132 | nan | 0.8132 | nan | 0.0 | 0.8132 | nan |
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+ | 0.1734 | 6.3 | 1260 | 0.1287 | 0.3686 | 0.7372 | 0.7372 | nan | 0.7372 | nan | 0.0 | 0.7372 | nan |
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+ | 0.4915 | 6.4 | 1280 | 0.1211 | 0.3790 | 0.7579 | 0.7579 | nan | 0.7579 | nan | 0.0 | 0.7579 | nan |
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+ | 0.0287 | 6.5 | 1300 | 0.1163 | 0.3962 | 0.7923 | 0.7923 | nan | 0.7923 | nan | 0.0 | 0.7923 | nan |
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+ | 0.0945 | 6.6 | 1320 | 0.1229 | 0.3702 | 0.7404 | 0.7404 | nan | 0.7404 | nan | 0.0 | 0.7404 | nan |
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+ | 0.0197 | 6.7 | 1340 | 0.1257 | 0.3603 | 0.7206 | 0.7206 | nan | 0.7206 | nan | 0.0 | 0.7206 | nan |
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+ | 0.0697 | 6.8 | 1360 | 0.1172 | 0.3980 | 0.7960 | 0.7960 | nan | 0.7960 | nan | 0.0 | 0.7960 | nan |
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+ | 0.0497 | 6.9 | 1380 | 0.1240 | 0.3768 | 0.7536 | 0.7536 | nan | 0.7536 | nan | 0.0 | 0.7536 | nan |
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+ | 0.0502 | 7.0 | 1400 | 0.1156 | 0.3873 | 0.7747 | 0.7747 | nan | 0.7747 | nan | 0.0 | 0.7747 | nan |
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+ | 0.0649 | 7.1 | 1420 | 0.1121 | 0.4073 | 0.8146 | 0.8146 | nan | 0.8146 | nan | 0.0 | 0.8146 | nan |
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+ | 0.0251 | 7.2 | 1440 | 0.1174 | 0.3908 | 0.7816 | 0.7816 | nan | 0.7816 | nan | 0.0 | 0.7816 | nan |
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+ | 0.0123 | 7.3 | 1460 | 0.1292 | 0.3724 | 0.7448 | 0.7448 | nan | 0.7448 | nan | 0.0 | 0.7448 | nan |
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+ | 0.0663 | 7.4 | 1480 | 0.1187 | 0.3987 | 0.7974 | 0.7974 | nan | 0.7974 | nan | 0.0 | 0.7974 | nan |
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+ | 0.1957 | 7.5 | 1500 | 0.1164 | 0.3768 | 0.7537 | 0.7537 | nan | 0.7537 | nan | 0.0 | 0.7537 | nan |
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+ | 0.113 | 7.6 | 1520 | 0.1188 | 0.3716 | 0.7432 | 0.7432 | nan | 0.7432 | nan | 0.0 | 0.7432 | nan |
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+ | 0.0586 | 7.7 | 1540 | 0.1091 | 0.3982 | 0.7965 | 0.7965 | nan | 0.7965 | nan | 0.0 | 0.7965 | nan |
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+ | 0.1121 | 7.8 | 1560 | 0.1200 | 0.3755 | 0.7510 | 0.7510 | nan | 0.7510 | nan | 0.0 | 0.7510 | nan |
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+ | 0.1066 | 7.9 | 1580 | 0.1222 | 0.3841 | 0.7681 | 0.7681 | nan | 0.7681 | nan | 0.0 | 0.7681 | nan |
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+ | 0.0185 | 8.0 | 1600 | 0.1191 | 0.3783 | 0.7566 | 0.7566 | nan | 0.7566 | nan | 0.0 | 0.7566 | nan |
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+ | 0.0281 | 8.1 | 1620 | 0.1191 | 0.4042 | 0.8083 | 0.8083 | nan | 0.8083 | nan | 0.0 | 0.8083 | nan |
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+ | 0.045 | 8.2 | 1640 | 0.1267 | 0.3699 | 0.7398 | 0.7398 | nan | 0.7398 | nan | 0.0 | 0.7398 | nan |
143
+ | 0.0271 | 8.3 | 1660 | 0.1199 | 0.3927 | 0.7853 | 0.7853 | nan | 0.7853 | nan | 0.0 | 0.7853 | nan |
144
+ | 0.0099 | 8.4 | 1680 | 0.1238 | 0.3864 | 0.7728 | 0.7728 | nan | 0.7728 | nan | 0.0 | 0.7728 | nan |
145
+ | 0.0513 | 8.5 | 1700 | 0.1284 | 0.3804 | 0.7607 | 0.7607 | nan | 0.7607 | nan | 0.0 | 0.7607 | nan |
146
+ | 0.066 | 8.6 | 1720 | 0.1239 | 0.3930 | 0.7861 | 0.7861 | nan | 0.7861 | nan | 0.0 | 0.7861 | nan |
147
+ | 0.018 | 8.7 | 1740 | 0.1230 | 0.3901 | 0.7802 | 0.7802 | nan | 0.7802 | nan | 0.0 | 0.7802 | nan |
148
+ | 0.0264 | 8.8 | 1760 | 0.1251 | 0.3948 | 0.7896 | 0.7896 | nan | 0.7896 | nan | 0.0 | 0.7896 | nan |
149
+ | 0.0241 | 8.9 | 1780 | 0.1328 | 0.3882 | 0.7763 | 0.7763 | nan | 0.7763 | nan | 0.0 | 0.7763 | nan |
150
+ | 0.0115 | 9.0 | 1800 | 0.1301 | 0.3933 | 0.7865 | 0.7865 | nan | 0.7865 | nan | 0.0 | 0.7865 | nan |
151
+ | 0.0318 | 9.1 | 1820 | 0.1246 | 0.3999 | 0.7997 | 0.7997 | nan | 0.7997 | nan | 0.0 | 0.7997 | nan |
152
+ | 0.2119 | 9.2 | 1840 | 0.1276 | 0.3817 | 0.7633 | 0.7633 | nan | 0.7633 | nan | 0.0 | 0.7633 | nan |
153
+ | 0.0689 | 9.3 | 1860 | 0.1202 | 0.4033 | 0.8067 | 0.8067 | nan | 0.8067 | nan | 0.0 | 0.8067 | nan |
154
+ | 0.0177 | 9.4 | 1880 | 0.1266 | 0.3917 | 0.7834 | 0.7834 | nan | 0.7834 | nan | 0.0 | 0.7834 | nan |
155
+ | 0.013 | 9.5 | 1900 | 0.1314 | 0.3776 | 0.7551 | 0.7551 | nan | 0.7551 | nan | 0.0 | 0.7551 | nan |
156
+ | 0.0174 | 9.6 | 1920 | 0.1246 | 0.3993 | 0.7987 | 0.7987 | nan | 0.7987 | nan | 0.0 | 0.7987 | nan |
157
+ | 0.0483 | 9.7 | 1940 | 0.1263 | 0.3885 | 0.7770 | 0.7770 | nan | 0.7770 | nan | 0.0 | 0.7770 | nan |
158
+ | 0.0495 | 9.8 | 1960 | 0.1253 | 0.3938 | 0.7875 | 0.7875 | nan | 0.7875 | nan | 0.0 | 0.7875 | nan |
159
+ | 0.0595 | 9.9 | 1980 | 0.1293 | 0.3863 | 0.7725 | 0.7725 | nan | 0.7725 | nan | 0.0 | 0.7725 | nan |
160
+ | 0.0454 | 10.0 | 2000 | 0.1274 | 0.3943 | 0.7885 | 0.7885 | nan | 0.7885 | nan | 0.0 | 0.7885 | nan |
161
+ | 0.018 | 10.1 | 2020 | 0.1276 | 0.3861 | 0.7723 | 0.7723 | nan | 0.7723 | nan | 0.0 | 0.7723 | nan |
162
+ | 0.0274 | 10.2 | 2040 | 0.1227 | 0.4016 | 0.8032 | 0.8032 | nan | 0.8032 | nan | 0.0 | 0.8032 | nan |
163
+ | 0.038 | 10.3 | 2060 | 0.1239 | 0.3862 | 0.7725 | 0.7725 | nan | 0.7725 | nan | 0.0 | 0.7725 | nan |
164
+ | 0.0459 | 10.4 | 2080 | 0.1288 | 0.3792 | 0.7584 | 0.7584 | nan | 0.7584 | nan | 0.0 | 0.7584 | nan |
165
+ | 0.0789 | 10.5 | 2100 | 0.1287 | 0.3886 | 0.7772 | 0.7772 | nan | 0.7772 | nan | 0.0 | 0.7772 | nan |
166
+ | 0.0424 | 10.6 | 2120 | 0.1325 | 0.3886 | 0.7772 | 0.7772 | nan | 0.7772 | nan | 0.0 | 0.7772 | nan |
167
+ | 0.0119 | 10.7 | 2140 | 0.1309 | 0.3952 | 0.7904 | 0.7904 | nan | 0.7904 | nan | 0.0 | 0.7904 | nan |
168
+ | 0.01 | 10.8 | 2160 | 0.1271 | 0.4017 | 0.8035 | 0.8035 | nan | 0.8035 | nan | 0.0 | 0.8035 | nan |
169
+ | 0.017 | 10.9 | 2180 | 0.1282 | 0.3904 | 0.7808 | 0.7808 | nan | 0.7808 | nan | 0.0 | 0.7808 | nan |
170
+ | 0.0324 | 11.0 | 2200 | 0.1228 | 0.4000 | 0.8000 | 0.8000 | nan | 0.8000 | nan | 0.0 | 0.8000 | nan |
171
+ | 0.0482 | 11.1 | 2220 | 0.1252 | 0.3968 | 0.7937 | 0.7937 | nan | 0.7937 | nan | 0.0 | 0.7937 | nan |
172
+ | 0.0291 | 11.2 | 2240 | 0.1272 | 0.3891 | 0.7782 | 0.7782 | nan | 0.7782 | nan | 0.0 | 0.7782 | nan |
173
+ | 0.1091 | 11.3 | 2260 | 0.1272 | 0.3956 | 0.7912 | 0.7912 | nan | 0.7912 | nan | 0.0 | 0.7912 | nan |
174
+ | 0.0189 | 11.4 | 2280 | 0.1310 | 0.3847 | 0.7693 | 0.7693 | nan | 0.7693 | nan | 0.0 | 0.7693 | nan |
175
+ | 0.0357 | 11.5 | 2300 | 0.1321 | 0.3823 | 0.7646 | 0.7646 | nan | 0.7646 | nan | 0.0 | 0.7646 | nan |
176
+ | 0.051 | 11.6 | 2320 | 0.1243 | 0.3987 | 0.7974 | 0.7974 | nan | 0.7974 | nan | 0.0 | 0.7974 | nan |
177
+ | 0.4267 | 11.7 | 2340 | 0.1273 | 0.3934 | 0.7869 | 0.7869 | nan | 0.7869 | nan | 0.0 | 0.7869 | nan |
178
+ | 0.0546 | 11.8 | 2360 | 0.1292 | 0.3884 | 0.7768 | 0.7768 | nan | 0.7768 | nan | 0.0 | 0.7768 | nan |
179
+ | 0.0249 | 11.9 | 2380 | 0.1281 | 0.3953 | 0.7905 | 0.7905 | nan | 0.7905 | nan | 0.0 | 0.7905 | nan |
180
+ | 0.0103 | 12.0 | 2400 | 0.1247 | 0.4042 | 0.8085 | 0.8085 | nan | 0.8085 | nan | 0.0 | 0.8085 | nan |
181
+ | 0.0145 | 12.1 | 2420 | 0.1273 | 0.3940 | 0.7880 | 0.7880 | nan | 0.7880 | nan | 0.0 | 0.7880 | nan |
182
+ | 0.0211 | 12.2 | 2440 | 0.1265 | 0.3951 | 0.7902 | 0.7902 | nan | 0.7902 | nan | 0.0 | 0.7902 | nan |
183
+ | 0.1905 | 12.3 | 2460 | 0.1258 | 0.3963 | 0.7925 | 0.7925 | nan | 0.7925 | nan | 0.0 | 0.7925 | nan |
184
+ | 0.0164 | 12.4 | 2480 | 0.1253 | 0.4028 | 0.8055 | 0.8055 | nan | 0.8055 | nan | 0.0 | 0.8055 | nan |
185
+ | 0.0659 | 12.5 | 2500 | 0.1251 | 0.4048 | 0.8097 | 0.8097 | nan | 0.8097 | nan | 0.0 | 0.8097 | nan |
186
+ | 0.2672 | 12.6 | 2520 | 0.1265 | 0.3931 | 0.7862 | 0.7862 | nan | 0.7862 | nan | 0.0 | 0.7862 | nan |
187
+ | 0.0145 | 12.7 | 2540 | 0.1277 | 0.3887 | 0.7774 | 0.7774 | nan | 0.7774 | nan | 0.0 | 0.7774 | nan |
188
+ | 0.0219 | 12.8 | 2560 | 0.1260 | 0.3960 | 0.7920 | 0.7920 | nan | 0.7920 | nan | 0.0 | 0.7920 | nan |
189
+ | 0.0372 | 12.9 | 2580 | 0.1271 | 0.3943 | 0.7886 | 0.7886 | nan | 0.7886 | nan | 0.0 | 0.7886 | nan |
190
+ | 0.0481 | 13.0 | 2600 | 0.1269 | 0.3975 | 0.7950 | 0.7950 | nan | 0.7950 | nan | 0.0 | 0.7950 | nan |
191
+
192
+
193
+ ### Framework versions
194
+
195
+ - Transformers 4.47.1
196
+ - Pytorch 2.5.1+cu124
197
+ - Datasets 3.2.0
198
+ - Tokenizers 0.21.0
 
 
 
 
 
 
 
 
 
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