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custom-object-masking_v4-2-1

Image Segmentation
Transformers
Safetensors
segformer
vision
Generated from Trainer
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  2. config.json +80 -0
  3. model.safetensors +3 -0
  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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-
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- <!-- Provide a longer summary of what this model is. -->
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-
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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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- - **Funded by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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-
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- ## Uses
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-
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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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-
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- ### Direct Use
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-
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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-
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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-
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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-
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical 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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-
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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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-
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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-
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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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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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-
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [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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- ### Compute Infrastructure
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- #### Hardware
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- ## Citation [optional]
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- ## Glossary [optional]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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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: custom-object-masking_v4-2-1
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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_v4-2-1
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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: 0.1002
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+ - Mean Iou: 0.4191
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+ - Mean Accuracy: 0.8383
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+ - Overall Accuracy: 0.8383
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+ - Accuracy Unknown: nan
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+ - Accuracy Background: 0.8383
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+ - Accuracy Object: nan
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+ - Iou Unknown: 0.0
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+ - Iou Background: 0.8383
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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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+ | 0.7391 | 0.0889 | 20 | 0.9385 | 0.2926 | 0.8778 | 0.8778 | nan | 0.8778 | nan | 0.0 | 0.8778 | 0.0 |
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+ | 0.6082 | 0.1778 | 40 | 0.7636 | 0.2813 | 0.8438 | 0.8438 | nan | 0.8438 | nan | 0.0 | 0.8438 | 0.0 |
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+ | 0.5773 | 0.2667 | 60 | 0.6362 | 0.4261 | 0.8523 | 0.8523 | nan | 0.8523 | nan | 0.0 | 0.8523 | nan |
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+ | 0.4076 | 0.3556 | 80 | 0.4904 | 0.3878 | 0.7756 | 0.7756 | nan | 0.7756 | nan | 0.0 | 0.7756 | nan |
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+ | 0.448 | 0.4444 | 100 | 0.3627 | 0.2691 | 0.5381 | 0.5381 | nan | 0.5381 | nan | 0.0 | 0.5381 | nan |
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+ | 0.3588 | 0.5333 | 120 | 0.3505 | 0.2945 | 0.5891 | 0.5891 | nan | 0.5891 | nan | 0.0 | 0.5891 | nan |
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+ | 0.3294 | 0.6222 | 140 | 0.2927 | 0.3202 | 0.6404 | 0.6404 | nan | 0.6404 | nan | 0.0 | 0.6404 | nan |
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+ | 0.2958 | 0.7111 | 160 | 0.2787 | 0.3285 | 0.6570 | 0.6570 | nan | 0.6570 | nan | 0.0 | 0.6570 | nan |
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+ | 0.1979 | 0.8 | 180 | 0.2575 | 0.3104 | 0.6207 | 0.6207 | nan | 0.6207 | nan | 0.0 | 0.6207 | nan |
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+ | 0.268 | 0.8889 | 200 | 0.2255 | 0.3344 | 0.6688 | 0.6688 | nan | 0.6688 | nan | 0.0 | 0.6688 | nan |
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+ | 0.415 | 0.9778 | 220 | 0.2097 | 0.3364 | 0.6729 | 0.6729 | nan | 0.6729 | nan | 0.0 | 0.6729 | nan |
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+ | 0.2191 | 1.0667 | 240 | 0.2244 | 0.3725 | 0.7450 | 0.7450 | nan | 0.7450 | nan | 0.0 | 0.7450 | nan |
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+ | 0.1481 | 1.1556 | 260 | 0.2998 | 0.4326 | 0.8651 | 0.8651 | nan | 0.8651 | nan | 0.0 | 0.8651 | nan |
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+ | 0.216 | 1.2444 | 280 | 0.1933 | 0.2995 | 0.5990 | 0.5990 | nan | 0.5990 | nan | 0.0 | 0.5990 | nan |
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+ | 0.1621 | 1.3333 | 300 | 0.2139 | 0.4043 | 0.8085 | 0.8085 | nan | 0.8085 | nan | 0.0 | 0.8085 | nan |
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+ | 0.2972 | 1.4222 | 320 | 0.1705 | 0.3139 | 0.6279 | 0.6279 | nan | 0.6279 | nan | 0.0 | 0.6279 | nan |
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+ | 0.2088 | 1.5111 | 340 | 0.2031 | 0.3358 | 0.6717 | 0.6717 | nan | 0.6717 | nan | 0.0 | 0.6717 | nan |
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+ | 0.1709 | 1.6 | 360 | 0.1721 | 0.3698 | 0.7396 | 0.7396 | nan | 0.7396 | nan | 0.0 | 0.7396 | nan |
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+ | 0.2445 | 1.6889 | 380 | 0.1624 | 0.3891 | 0.7781 | 0.7781 | nan | 0.7781 | nan | 0.0 | 0.7781 | nan |
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+ | 0.4145 | 1.7778 | 400 | 0.2073 | 0.1587 | 0.3175 | 0.3175 | nan | 0.3175 | nan | 0.0 | 0.3175 | nan |
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+ | 0.2236 | 1.8667 | 420 | 0.1698 | 0.4168 | 0.8336 | 0.8336 | nan | 0.8336 | nan | 0.0 | 0.8336 | nan |
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+ | 0.0865 | 1.9556 | 440 | 0.1430 | 0.3734 | 0.7467 | 0.7467 | nan | 0.7467 | nan | 0.0 | 0.7467 | nan |
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+ | 0.1752 | 2.0444 | 460 | 0.1378 | 0.3876 | 0.7752 | 0.7752 | nan | 0.7752 | nan | 0.0 | 0.7752 | nan |
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+ | 0.0611 | 2.1333 | 480 | 0.1409 | 0.3283 | 0.6565 | 0.6565 | nan | 0.6565 | nan | 0.0 | 0.6565 | nan |
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+ | 0.1119 | 2.2222 | 500 | 0.1314 | 0.3812 | 0.7624 | 0.7624 | nan | 0.7624 | nan | 0.0 | 0.7624 | nan |
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+ | 0.1539 | 2.3111 | 520 | 0.1198 | 0.3732 | 0.7463 | 0.7463 | nan | 0.7463 | nan | 0.0 | 0.7463 | nan |
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+ | 0.1989 | 2.4 | 540 | 0.1192 | 0.3706 | 0.7412 | 0.7412 | nan | 0.7412 | nan | 0.0 | 0.7412 | nan |
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+ | 0.1238 | 2.4889 | 560 | 0.1178 | 0.3815 | 0.7630 | 0.7630 | nan | 0.7630 | nan | 0.0 | 0.7630 | nan |
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+ | 0.15 | 2.5778 | 580 | 0.1279 | 0.3462 | 0.6924 | 0.6924 | nan | 0.6924 | nan | 0.0 | 0.6924 | nan |
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+ | 0.1976 | 2.6667 | 600 | 0.1296 | 0.3957 | 0.7914 | 0.7914 | nan | 0.7914 | nan | 0.0 | 0.7914 | nan |
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+ | 0.0968 | 2.7556 | 620 | 0.1160 | 0.3757 | 0.7515 | 0.7515 | nan | 0.7515 | nan | 0.0 | 0.7515 | nan |
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+ | 0.0915 | 2.8444 | 640 | 0.1122 | 0.4019 | 0.8039 | 0.8039 | nan | 0.8039 | nan | 0.0 | 0.8039 | nan |
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+ | 0.2159 | 2.9333 | 660 | 0.1099 | 0.3695 | 0.7390 | 0.7390 | nan | 0.7390 | nan | 0.0 | 0.7390 | nan |
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+ | 0.0781 | 3.0222 | 680 | 0.1184 | 0.3871 | 0.7743 | 0.7743 | nan | 0.7743 | nan | 0.0 | 0.7743 | nan |
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+ | 0.1362 | 3.1111 | 700 | 0.1061 | 0.3861 | 0.7723 | 0.7723 | nan | 0.7723 | nan | 0.0 | 0.7723 | nan |
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+ | 0.0367 | 3.2 | 720 | 0.1273 | 0.3252 | 0.6503 | 0.6503 | nan | 0.6503 | nan | 0.0 | 0.6503 | nan |
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+ | 0.0451 | 3.2889 | 740 | 0.1225 | 0.4195 | 0.8389 | 0.8389 | nan | 0.8389 | nan | 0.0 | 0.8389 | nan |
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+ | 0.0689 | 3.3778 | 760 | 0.1103 | 0.3794 | 0.7587 | 0.7587 | nan | 0.7587 | nan | 0.0 | 0.7587 | nan |
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+ | 0.2043 | 3.4667 | 780 | 0.1085 | 0.3551 | 0.7101 | 0.7101 | nan | 0.7101 | nan | 0.0 | 0.7101 | nan |
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+ | 0.0473 | 3.5556 | 800 | 0.1085 | 0.3911 | 0.7822 | 0.7822 | nan | 0.7822 | nan | 0.0 | 0.7822 | nan |
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+ | 0.0904 | 3.6444 | 820 | 0.1091 | 0.3995 | 0.7990 | 0.7990 | nan | 0.7990 | nan | 0.0 | 0.7990 | nan |
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+ | 0.1193 | 3.7333 | 840 | 0.1083 | 0.3949 | 0.7898 | 0.7898 | nan | 0.7898 | nan | 0.0 | 0.7898 | nan |
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+ | 0.0279 | 3.8222 | 860 | 0.1040 | 0.4008 | 0.8016 | 0.8016 | nan | 0.8016 | nan | 0.0 | 0.8016 | nan |
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+ | 0.0474 | 3.9111 | 880 | 0.1077 | 0.3612 | 0.7225 | 0.7225 | nan | 0.7225 | nan | 0.0 | 0.7225 | nan |
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+ | 0.2733 | 4.0 | 900 | 0.1034 | 0.4099 | 0.8198 | 0.8198 | nan | 0.8198 | nan | 0.0 | 0.8198 | nan |
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+ | 0.0789 | 4.0889 | 920 | 0.0995 | 0.4071 | 0.8142 | 0.8142 | nan | 0.8142 | nan | 0.0 | 0.8142 | nan |
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+ | 0.1098 | 4.1778 | 940 | 0.1028 | 0.3983 | 0.7966 | 0.7966 | nan | 0.7966 | nan | 0.0 | 0.7966 | nan |
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+ | 0.0274 | 4.2667 | 960 | 0.1020 | 0.3771 | 0.7542 | 0.7542 | nan | 0.7542 | nan | 0.0 | 0.7542 | nan |
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+ | 0.1645 | 4.3556 | 980 | 0.1147 | 0.4270 | 0.8540 | 0.8540 | nan | 0.8540 | nan | 0.0 | 0.8540 | nan |
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+ | 0.0508 | 4.4444 | 1000 | 0.1008 | 0.3972 | 0.7944 | 0.7944 | nan | 0.7944 | nan | 0.0 | 0.7944 | nan |
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+ | 0.1336 | 4.5333 | 1020 | 0.0963 | 0.3973 | 0.7947 | 0.7947 | nan | 0.7947 | nan | 0.0 | 0.7947 | nan |
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+ | 0.0346 | 4.6222 | 1040 | 0.1061 | 0.3759 | 0.7519 | 0.7519 | nan | 0.7519 | nan | 0.0 | 0.7519 | nan |
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+ | 0.0243 | 4.7111 | 1060 | 0.1017 | 0.4157 | 0.8315 | 0.8315 | nan | 0.8315 | nan | 0.0 | 0.8315 | nan |
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+ | 0.2029 | 4.8 | 1080 | 0.1016 | 0.3618 | 0.7235 | 0.7235 | nan | 0.7235 | nan | 0.0 | 0.7235 | nan |
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+ | 0.0348 | 4.8889 | 1100 | 0.1028 | 0.4353 | 0.8707 | 0.8707 | nan | 0.8707 | nan | 0.0 | 0.8707 | nan |
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+ | 0.0641 | 4.9778 | 1120 | 0.1023 | 0.4124 | 0.8249 | 0.8249 | nan | 0.8249 | nan | 0.0 | 0.8249 | nan |
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+ | 0.1042 | 5.0667 | 1140 | 0.1034 | 0.3725 | 0.7450 | 0.7450 | nan | 0.7450 | nan | 0.0 | 0.7450 | nan |
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+ | 0.034 | 5.1556 | 1160 | 0.0995 | 0.4246 | 0.8493 | 0.8493 | nan | 0.8493 | nan | 0.0 | 0.8493 | nan |
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+ | 0.065 | 5.2444 | 1180 | 0.0954 | 0.3963 | 0.7926 | 0.7926 | nan | 0.7926 | nan | 0.0 | 0.7926 | nan |
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+ | 0.0585 | 5.3333 | 1200 | 0.0981 | 0.4203 | 0.8407 | 0.8407 | nan | 0.8407 | nan | 0.0 | 0.8407 | nan |
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+ | 0.0584 | 5.4222 | 1220 | 0.1002 | 0.4252 | 0.8504 | 0.8504 | nan | 0.8504 | nan | 0.0 | 0.8504 | nan |
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+ | 0.0546 | 5.5111 | 1240 | 0.0923 | 0.4067 | 0.8134 | 0.8134 | nan | 0.8134 | nan | 0.0 | 0.8134 | nan |
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+ | 0.09 | 5.6 | 1260 | 0.1025 | 0.4196 | 0.8392 | 0.8392 | nan | 0.8392 | nan | 0.0 | 0.8392 | nan |
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+ | 0.2459 | 5.6889 | 1280 | 0.1104 | 0.4421 | 0.8843 | 0.8843 | nan | 0.8843 | nan | 0.0 | 0.8843 | nan |
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+ | 0.0388 | 5.7778 | 1300 | 0.0958 | 0.4003 | 0.8006 | 0.8006 | nan | 0.8006 | nan | 0.0 | 0.8006 | nan |
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+ | 0.0286 | 5.8667 | 1320 | 0.1013 | 0.4067 | 0.8135 | 0.8135 | nan | 0.8135 | nan | 0.0 | 0.8135 | nan |
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+ | 0.0743 | 5.9556 | 1340 | 0.1011 | 0.4149 | 0.8298 | 0.8298 | nan | 0.8298 | nan | 0.0 | 0.8298 | nan |
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+ | 0.0701 | 6.0444 | 1360 | 0.0981 | 0.4147 | 0.8294 | 0.8294 | nan | 0.8294 | nan | 0.0 | 0.8294 | nan |
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+ | 0.0132 | 6.1333 | 1380 | 0.0923 | 0.4010 | 0.8021 | 0.8021 | nan | 0.8021 | nan | 0.0 | 0.8021 | nan |
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+ | 0.0445 | 6.2222 | 1400 | 0.0925 | 0.4068 | 0.8135 | 0.8135 | nan | 0.8135 | nan | 0.0 | 0.8135 | nan |
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+ | 0.0202 | 6.3111 | 1420 | 0.0940 | 0.4121 | 0.8242 | 0.8242 | nan | 0.8242 | nan | 0.0 | 0.8242 | nan |
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+ | 0.062 | 6.4 | 1440 | 0.0940 | 0.4258 | 0.8515 | 0.8515 | nan | 0.8515 | nan | 0.0 | 0.8515 | nan |
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+ | 0.1998 | 6.4889 | 1460 | 0.0976 | 0.3918 | 0.7837 | 0.7837 | nan | 0.7837 | nan | 0.0 | 0.7837 | nan |
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+ | 0.0473 | 6.5778 | 1480 | 0.0934 | 0.4260 | 0.8519 | 0.8519 | nan | 0.8519 | nan | 0.0 | 0.8519 | nan |
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+ | 0.0236 | 6.6667 | 1500 | 0.0927 | 0.4082 | 0.8163 | 0.8163 | nan | 0.8163 | nan | 0.0 | 0.8163 | nan |
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+ | 0.052 | 6.7556 | 1520 | 0.0958 | 0.4066 | 0.8132 | 0.8132 | nan | 0.8132 | nan | 0.0 | 0.8132 | nan |
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+ | 0.0258 | 6.8444 | 1540 | 0.0925 | 0.4243 | 0.8485 | 0.8485 | nan | 0.8485 | nan | 0.0 | 0.8485 | nan |
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+ | 0.0357 | 6.9333 | 1560 | 0.0927 | 0.4082 | 0.8163 | 0.8163 | nan | 0.8163 | nan | 0.0 | 0.8163 | nan |
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+ | 0.0122 | 7.0222 | 1580 | 0.0956 | 0.4246 | 0.8492 | 0.8492 | nan | 0.8492 | nan | 0.0 | 0.8492 | nan |
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+ | 0.1689 | 7.1111 | 1600 | 0.0913 | 0.4144 | 0.8288 | 0.8288 | nan | 0.8288 | nan | 0.0 | 0.8288 | nan |
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+ | 0.0657 | 7.2 | 1620 | 0.0952 | 0.4067 | 0.8134 | 0.8134 | nan | 0.8134 | nan | 0.0 | 0.8134 | nan |
142
+ | 0.1023 | 7.2889 | 1640 | 0.0957 | 0.4116 | 0.8232 | 0.8232 | nan | 0.8232 | nan | 0.0 | 0.8232 | nan |
143
+ | 0.0202 | 7.3778 | 1660 | 0.0910 | 0.4090 | 0.8180 | 0.8180 | nan | 0.8180 | nan | 0.0 | 0.8180 | nan |
144
+ | 0.0802 | 7.4667 | 1680 | 0.0934 | 0.4091 | 0.8183 | 0.8183 | nan | 0.8183 | nan | 0.0 | 0.8183 | nan |
145
+ | 0.0925 | 7.5556 | 1700 | 0.0926 | 0.4131 | 0.8262 | 0.8262 | nan | 0.8262 | nan | 0.0 | 0.8262 | nan |
146
+ | 0.0154 | 7.6444 | 1720 | 0.0958 | 0.4140 | 0.8280 | 0.8280 | nan | 0.8280 | nan | 0.0 | 0.8280 | nan |
147
+ | 0.0176 | 7.7333 | 1740 | 0.0923 | 0.4174 | 0.8348 | 0.8348 | nan | 0.8348 | nan | 0.0 | 0.8348 | nan |
148
+ | 0.0861 | 7.8222 | 1760 | 0.0978 | 0.4234 | 0.8468 | 0.8468 | nan | 0.8468 | nan | 0.0 | 0.8468 | nan |
149
+ | 0.0422 | 7.9111 | 1780 | 0.0971 | 0.4085 | 0.8171 | 0.8171 | nan | 0.8171 | nan | 0.0 | 0.8171 | nan |
150
+ | 0.287 | 8.0 | 1800 | 0.1010 | 0.4154 | 0.8308 | 0.8308 | nan | 0.8308 | nan | 0.0 | 0.8308 | nan |
151
+ | 0.2216 | 8.0889 | 1820 | 0.0991 | 0.4037 | 0.8073 | 0.8073 | nan | 0.8073 | nan | 0.0 | 0.8073 | nan |
152
+ | 0.0269 | 8.1778 | 1840 | 0.0993 | 0.4258 | 0.8515 | 0.8515 | nan | 0.8515 | nan | 0.0 | 0.8515 | nan |
153
+ | 0.0176 | 8.2667 | 1860 | 0.1009 | 0.4232 | 0.8464 | 0.8464 | nan | 0.8464 | nan | 0.0 | 0.8464 | nan |
154
+ | 0.0371 | 8.3556 | 1880 | 0.0944 | 0.4162 | 0.8323 | 0.8323 | nan | 0.8323 | nan | 0.0 | 0.8323 | nan |
155
+ | 0.0406 | 8.4444 | 1900 | 0.1013 | 0.3907 | 0.7814 | 0.7814 | nan | 0.7814 | nan | 0.0 | 0.7814 | nan |
156
+ | 0.0286 | 8.5333 | 1920 | 0.1015 | 0.4215 | 0.8430 | 0.8430 | nan | 0.8430 | nan | 0.0 | 0.8430 | nan |
157
+ | 0.0307 | 8.6222 | 1940 | 0.0967 | 0.4213 | 0.8426 | 0.8426 | nan | 0.8426 | nan | 0.0 | 0.8426 | nan |
158
+ | 0.0372 | 8.7111 | 1960 | 0.0938 | 0.4166 | 0.8332 | 0.8332 | nan | 0.8332 | nan | 0.0 | 0.8332 | nan |
159
+ | 0.0094 | 8.8 | 1980 | 0.0965 | 0.4333 | 0.8665 | 0.8665 | nan | 0.8665 | nan | 0.0 | 0.8665 | nan |
160
+ | 0.0469 | 8.8889 | 2000 | 0.0995 | 0.3927 | 0.7855 | 0.7855 | nan | 0.7855 | nan | 0.0 | 0.7855 | nan |
161
+ | 0.0172 | 8.9778 | 2020 | 0.0995 | 0.4123 | 0.8245 | 0.8245 | nan | 0.8245 | nan | 0.0 | 0.8245 | nan |
162
+ | 0.1531 | 9.0667 | 2040 | 0.1000 | 0.4047 | 0.8094 | 0.8094 | nan | 0.8094 | nan | 0.0 | 0.8094 | nan |
163
+ | 0.0339 | 9.1556 | 2060 | 0.1010 | 0.4271 | 0.8541 | 0.8541 | nan | 0.8541 | nan | 0.0 | 0.8541 | nan |
164
+ | 0.0364 | 9.2444 | 2080 | 0.0989 | 0.4048 | 0.8095 | 0.8095 | nan | 0.8095 | nan | 0.0 | 0.8095 | nan |
165
+ | 0.0554 | 9.3333 | 2100 | 0.0939 | 0.4216 | 0.8433 | 0.8433 | nan | 0.8433 | nan | 0.0 | 0.8433 | nan |
166
+ | 0.0416 | 9.4222 | 2120 | 0.0953 | 0.4184 | 0.8367 | 0.8367 | nan | 0.8367 | nan | 0.0 | 0.8367 | nan |
167
+ | 0.0386 | 9.5111 | 2140 | 0.0948 | 0.4197 | 0.8394 | 0.8394 | nan | 0.8394 | nan | 0.0 | 0.8394 | nan |
168
+ | 0.044 | 9.6 | 2160 | 0.0953 | 0.4212 | 0.8424 | 0.8424 | nan | 0.8424 | nan | 0.0 | 0.8424 | nan |
169
+ | 0.1638 | 9.6889 | 2180 | 0.0969 | 0.4179 | 0.8359 | 0.8359 | nan | 0.8359 | nan | 0.0 | 0.8359 | nan |
170
+ | 0.0155 | 9.7778 | 2200 | 0.0998 | 0.3974 | 0.7949 | 0.7949 | nan | 0.7949 | nan | 0.0 | 0.7949 | nan |
171
+ | 0.0232 | 9.8667 | 2220 | 0.0995 | 0.4064 | 0.8128 | 0.8128 | nan | 0.8128 | nan | 0.0 | 0.8128 | nan |
172
+ | 0.0476 | 9.9556 | 2240 | 0.0985 | 0.4146 | 0.8293 | 0.8293 | nan | 0.8293 | nan | 0.0 | 0.8293 | nan |
173
+ | 0.0567 | 10.0444 | 2260 | 0.1030 | 0.4062 | 0.8125 | 0.8125 | nan | 0.8125 | nan | 0.0 | 0.8125 | nan |
174
+ | 0.0489 | 10.1333 | 2280 | 0.1006 | 0.4080 | 0.8160 | 0.8160 | nan | 0.8160 | nan | 0.0 | 0.8160 | nan |
175
+ | 0.022 | 10.2222 | 2300 | 0.0994 | 0.4207 | 0.8414 | 0.8414 | nan | 0.8414 | nan | 0.0 | 0.8414 | nan |
176
+ | 0.0144 | 10.3111 | 2320 | 0.1023 | 0.4154 | 0.8308 | 0.8308 | nan | 0.8308 | nan | 0.0 | 0.8308 | nan |
177
+ | 0.0471 | 10.4 | 2340 | 0.1000 | 0.4102 | 0.8204 | 0.8204 | nan | 0.8204 | nan | 0.0 | 0.8204 | nan |
178
+ | 0.0483 | 10.4889 | 2360 | 0.1005 | 0.4213 | 0.8426 | 0.8426 | nan | 0.8426 | nan | 0.0 | 0.8426 | nan |
179
+ | 0.0064 | 10.5778 | 2380 | 0.1043 | 0.4138 | 0.8277 | 0.8277 | nan | 0.8277 | nan | 0.0 | 0.8277 | nan |
180
+ | 0.0526 | 10.6667 | 2400 | 0.1051 | 0.4082 | 0.8163 | 0.8163 | nan | 0.8163 | nan | 0.0 | 0.8163 | nan |
181
+ | 0.0301 | 10.7556 | 2420 | 0.1008 | 0.4110 | 0.8219 | 0.8219 | nan | 0.8219 | nan | 0.0 | 0.8219 | nan |
182
+ | 0.0297 | 10.8444 | 2440 | 0.0999 | 0.4073 | 0.8146 | 0.8146 | nan | 0.8146 | nan | 0.0 | 0.8146 | nan |
183
+ | 0.0377 | 10.9333 | 2460 | 0.1004 | 0.4147 | 0.8295 | 0.8295 | nan | 0.8295 | nan | 0.0 | 0.8295 | nan |
184
+ | 0.0673 | 11.0222 | 2480 | 0.0989 | 0.4175 | 0.8350 | 0.8350 | nan | 0.8350 | nan | 0.0 | 0.8350 | nan |
185
+ | 0.085 | 11.1111 | 2500 | 0.1012 | 0.4246 | 0.8492 | 0.8492 | nan | 0.8492 | nan | 0.0 | 0.8492 | nan |
186
+ | 0.0127 | 11.2 | 2520 | 0.0980 | 0.4230 | 0.8460 | 0.8460 | nan | 0.8460 | nan | 0.0 | 0.8460 | nan |
187
+ | 0.0083 | 11.2889 | 2540 | 0.0967 | 0.4241 | 0.8482 | 0.8482 | nan | 0.8482 | nan | 0.0 | 0.8482 | nan |
188
+ | 0.0216 | 11.3778 | 2560 | 0.0973 | 0.4276 | 0.8553 | 0.8553 | nan | 0.8553 | nan | 0.0 | 0.8553 | nan |
189
+ | 0.0376 | 11.4667 | 2580 | 0.1012 | 0.4276 | 0.8553 | 0.8553 | nan | 0.8553 | nan | 0.0 | 0.8553 | nan |
190
+ | 0.0623 | 11.5556 | 2600 | 0.1009 | 0.4270 | 0.8541 | 0.8541 | nan | 0.8541 | nan | 0.0 | 0.8541 | nan |
191
+ | 0.0276 | 11.6444 | 2620 | 0.1003 | 0.4142 | 0.8284 | 0.8284 | nan | 0.8284 | nan | 0.0 | 0.8284 | nan |
192
+ | 0.0336 | 11.7333 | 2640 | 0.1004 | 0.4046 | 0.8092 | 0.8092 | nan | 0.8092 | nan | 0.0 | 0.8092 | nan |
193
+ | 0.0307 | 11.8222 | 2660 | 0.1012 | 0.4019 | 0.8039 | 0.8039 | nan | 0.8039 | nan | 0.0 | 0.8039 | nan |
194
+ | 0.1815 | 11.9111 | 2680 | 0.1001 | 0.4101 | 0.8203 | 0.8203 | nan | 0.8203 | nan | 0.0 | 0.8203 | nan |
195
+ | 0.0171 | 12.0 | 2700 | 0.1002 | 0.4124 | 0.8249 | 0.8249 | nan | 0.8249 | nan | 0.0 | 0.8249 | nan |
196
+ | 0.0646 | 12.0889 | 2720 | 0.0997 | 0.4129 | 0.8257 | 0.8257 | nan | 0.8257 | nan | 0.0 | 0.8257 | nan |
197
+ | 0.051 | 12.1778 | 2740 | 0.0983 | 0.4163 | 0.8327 | 0.8327 | nan | 0.8327 | nan | 0.0 | 0.8327 | nan |
198
+ | 0.0502 | 12.2667 | 2760 | 0.1004 | 0.4217 | 0.8434 | 0.8434 | nan | 0.8434 | nan | 0.0 | 0.8434 | nan |
199
+ | 0.0077 | 12.3556 | 2780 | 0.0989 | 0.4194 | 0.8388 | 0.8388 | nan | 0.8388 | nan | 0.0 | 0.8388 | nan |
200
+ | 0.0212 | 12.4444 | 2800 | 0.0991 | 0.4162 | 0.8325 | 0.8325 | nan | 0.8325 | nan | 0.0 | 0.8325 | nan |
201
+ | 0.0313 | 12.5333 | 2820 | 0.1008 | 0.4116 | 0.8231 | 0.8231 | nan | 0.8231 | nan | 0.0 | 0.8231 | nan |
202
+ | 0.0064 | 12.6222 | 2840 | 0.1021 | 0.4199 | 0.8398 | 0.8398 | nan | 0.8398 | nan | 0.0 | 0.8398 | nan |
203
+ | 0.1206 | 12.7111 | 2860 | 0.1018 | 0.4198 | 0.8397 | 0.8397 | nan | 0.8397 | nan | 0.0 | 0.8397 | nan |
204
+ | 0.0261 | 12.8 | 2880 | 0.1031 | 0.4237 | 0.8475 | 0.8475 | nan | 0.8475 | nan | 0.0 | 0.8475 | nan |
205
+ | 0.0139 | 12.8889 | 2900 | 0.1024 | 0.4215 | 0.8429 | 0.8429 | nan | 0.8429 | nan | 0.0 | 0.8429 | nan |
206
+ | 0.0173 | 12.9778 | 2920 | 0.1002 | 0.4191 | 0.8383 | 0.8383 | nan | 0.8383 | nan | 0.0 | 0.8383 | nan |
207
+
208
+
209
+ ### Framework versions
210
+
211
+ - Transformers 4.47.1
212
+ - Pytorch 2.5.1+cu124
213
+ - Datasets 3.2.0
214
+ - Tokenizers 0.21.0
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