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peldrak
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segformer-b4-ade-finetuned-grCoastline

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  1. README.md +129 -198
  2. config.json +88 -0
  3. model.safetensors +3 -0
  4. runs/Mar06_16-09-18_bf4bc103b070/events.out.tfevents.1709741364.bf4bc103b070.25.0 +3 -0
  5. training_args.bin +3 -0
README.md CHANGED
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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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- 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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- ## Bias, Risks, and Limitations
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- ## How to Get Started with the Model
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- ## Training Details
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- ### Training Data
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- ### Training Procedure
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- #### Preprocessing [optional]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- ## Evaluation
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- ## Technical Specifications [optional]
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  ---
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+ license: other
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+ base_model: nvidia/segformer-b4-finetuned-ade-512-512
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+ tags:
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+ - vision
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+ - image-segmentation
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+ - generated_from_trainer
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+ model-index:
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+ - name: segformer-b4-ade-finetuned-grCoastline
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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-b4-ade-finetuned-grCoastline
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+ This model is a fine-tuned version of [nvidia/segformer-b4-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b4-finetuned-ade-512-512) on the peldrak/grCoastline_512 dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.2116
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+ - Mean Iou: 0.7207
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+ - Mean Accuracy: 0.7864
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+ - Overall Accuracy: 0.9441
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+ - Accuracy Water: 0.9915
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+ - Accuracy Whitewater: 0.0
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+ - Accuracy Sediment: 0.9083
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+ - Accuracy Other Natural Terrain: 0.8596
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+ - Accuracy Vegetation: 0.8771
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+ - Accuracy Development: 0.8701
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+ - Accuracy Unknown: 0.9983
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+ - Iou Water: 0.9580
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+ - Iou Whitewater: 0.0
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+ - Iou Sediment: 0.8691
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+ - Iou Other Natural Terrain: 0.7108
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+ - Iou Vegetation: 0.8236
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+ - Iou Development: 0.6878
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+ - Iou Unknown: 0.9957
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+ - F1 Score: 0.9437
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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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+ 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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+ The following hyperparameters were used during training:
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+ - learning_rate: 6e-05
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+ - train_batch_size: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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+ - lr_scheduler_type: linear
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+ - num_epochs: 30
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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 Water | Accuracy Whitewater | Accuracy Sediment | Accuracy Other Natural Terrain | Accuracy Vegetation | Accuracy Development | Accuracy Unknown | Iou Water | Iou Whitewater | Iou Sediment | Iou Other Natural Terrain | Iou Vegetation | Iou Development | Iou Unknown | F1 Score |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:--------------:|:-------------------:|:-----------------:|:------------------------------:|:-------------------:|:--------------------:|:----------------:|:---------:|:--------------:|:------------:|:-------------------------:|:--------------:|:---------------:|:-----------:|:--------:|
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+ | 1.4532 | 0.24 | 20 | 1.3435 | 0.4211 | 0.5264 | 0.7441 | 0.7775 | 0.0 | 0.2537 | 0.5410 | 0.7502 | 0.3664 | 0.9961 | 0.6995 | 0.0 | 0.2230 | 0.3135 | 0.4349 | 0.3008 | 0.9758 | 0.7366 |
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+ | 1.0467 | 0.49 | 40 | 0.8901 | 0.4799 | 0.5609 | 0.8306 | 0.9261 | 0.0 | 0.6581 | 0.3109 | 0.9513 | 0.0809 | 0.9992 | 0.8704 | 0.0 | 0.5553 | 0.3049 | 0.5627 | 0.0804 | 0.9860 | 0.8109 |
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+ | 0.9494 | 0.73 | 60 | 0.6358 | 0.5209 | 0.5972 | 0.8680 | 0.9788 | 0.0 | 0.8070 | 0.4142 | 0.9622 | 0.0220 | 0.9964 | 0.9044 | 0.0 | 0.6693 | 0.4008 | 0.6578 | 0.0220 | 0.9921 | 0.8452 |
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+ | 0.6118 | 0.98 | 80 | 0.5235 | 0.5454 | 0.6191 | 0.8834 | 0.9836 | 0.0 | 0.8558 | 0.5071 | 0.9635 | 0.0283 | 0.9951 | 0.9172 | 0.0 | 0.6969 | 0.4748 | 0.7090 | 0.0282 | 0.9914 | 0.8625 |
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+ | 0.5458 | 1.22 | 100 | 0.4739 | 0.5846 | 0.6514 | 0.8875 | 0.9514 | 0.0 | 0.8431 | 0.5217 | 0.9639 | 0.2829 | 0.9967 | 0.9258 | 0.0 | 0.7593 | 0.4726 | 0.6684 | 0.2728 | 0.9930 | 0.8793 |
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+ | 0.5935 | 1.46 | 120 | 0.3786 | 0.6090 | 0.6793 | 0.9055 | 0.9774 | 0.0 | 0.9195 | 0.7271 | 0.8854 | 0.2475 | 0.9980 | 0.9221 | 0.0 | 0.7820 | 0.5782 | 0.7474 | 0.2415 | 0.9921 | 0.8969 |
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+ | 0.616 | 1.71 | 140 | 0.3630 | 0.6382 | 0.7043 | 0.9141 | 0.9845 | 0.0 | 0.9106 | 0.7205 | 0.8929 | 0.4243 | 0.9975 | 0.9132 | 0.0 | 0.7515 | 0.6445 | 0.7799 | 0.3859 | 0.9920 | 0.9090 |
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+ | 0.6891 | 1.95 | 160 | 0.3563 | 0.6249 | 0.6897 | 0.9049 | 0.9912 | 0.0 | 0.8575 | 0.5156 | 0.9541 | 0.5139 | 0.9955 | 0.9010 | 0.0 | 0.7908 | 0.4895 | 0.7447 | 0.4577 | 0.9906 | 0.8978 |
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+ | 0.3915 | 2.2 | 180 | 0.3039 | 0.6420 | 0.7094 | 0.9122 | 0.9690 | 0.0 | 0.8829 | 0.7779 | 0.8809 | 0.4559 | 0.9990 | 0.9296 | 0.0 | 0.8211 | 0.5859 | 0.7445 | 0.4225 | 0.9901 | 0.9093 |
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+ | 0.478 | 2.44 | 200 | 0.2957 | 0.6661 | 0.7380 | 0.9201 | 0.9884 | 0.0 | 0.8179 | 0.8009 | 0.8854 | 0.6765 | 0.9973 | 0.9150 | 0.0 | 0.7697 | 0.6348 | 0.7986 | 0.5532 | 0.9916 | 0.9186 |
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+ | 0.2891 | 2.68 | 220 | 0.2687 | 0.6781 | 0.7439 | 0.9258 | 0.9890 | 0.0 | 0.9161 | 0.7389 | 0.8977 | 0.6776 | 0.9881 | 0.9253 | 0.0 | 0.8384 | 0.6330 | 0.7898 | 0.5726 | 0.9874 | 0.9238 |
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+ | 0.3432 | 2.93 | 240 | 0.2629 | 0.6725 | 0.7379 | 0.9246 | 0.9752 | 0.0 | 0.9444 | 0.7407 | 0.8825 | 0.6246 | 0.9976 | 0.9308 | 0.0 | 0.8389 | 0.6136 | 0.7804 | 0.5506 | 0.9935 | 0.9224 |
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+ | 0.4379 | 3.17 | 260 | 0.2528 | 0.6787 | 0.7360 | 0.9283 | 0.9837 | 0.0 | 0.9170 | 0.7116 | 0.9280 | 0.6139 | 0.9974 | 0.9422 | 0.0 | 0.8382 | 0.6218 | 0.7870 | 0.5687 | 0.9931 | 0.9255 |
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+ | 0.2129 | 3.41 | 280 | 0.2250 | 0.6775 | 0.7401 | 0.9264 | 0.9911 | 0.0 | 0.9001 | 0.6653 | 0.9301 | 0.7006 | 0.9936 | 0.9330 | 0.0 | 0.8456 | 0.6021 | 0.7849 | 0.5853 | 0.9918 | 0.9237 |
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+ | 0.1941 | 3.66 | 300 | 0.2191 | 0.6943 | 0.7621 | 0.9336 | 0.9770 | 0.0 | 0.9426 | 0.8365 | 0.8619 | 0.7193 | 0.9974 | 0.9377 | 0.0 | 0.8296 | 0.6934 | 0.8076 | 0.5981 | 0.9938 | 0.9325 |
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+ | 0.3357 | 3.9 | 320 | 0.2315 | 0.6841 | 0.7582 | 0.9267 | 0.9782 | 0.0 | 0.8671 | 0.7910 | 0.8801 | 0.7948 | 0.9961 | 0.9322 | 0.0 | 0.8089 | 0.6298 | 0.8011 | 0.6240 | 0.9926 | 0.9261 |
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+ | 0.2456 | 4.15 | 340 | 0.2462 | 0.6904 | 0.7586 | 0.9303 | 0.9895 | 0.0 | 0.9229 | 0.6965 | 0.8897 | 0.8146 | 0.9970 | 0.9339 | 0.0 | 0.8563 | 0.6091 | 0.7915 | 0.6476 | 0.9947 | 0.9284 |
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+ | 0.3712 | 4.39 | 360 | 0.2366 | 0.6918 | 0.7696 | 0.9307 | 0.9848 | 0.0 | 0.8491 | 0.8504 | 0.8689 | 0.8389 | 0.9951 | 0.9514 | 0.0 | 0.8085 | 0.6517 | 0.8102 | 0.6272 | 0.9935 | 0.9311 |
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+ | 0.4034 | 4.63 | 380 | 0.2442 | 0.6854 | 0.7691 | 0.9254 | 0.9799 | 0.0 | 0.9505 | 0.8457 | 0.7848 | 0.8289 | 0.9940 | 0.9361 | 0.0 | 0.8287 | 0.6650 | 0.7533 | 0.6215 | 0.9930 | 0.9248 |
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+ | 0.1794 | 4.88 | 400 | 0.2162 | 0.6951 | 0.7555 | 0.9338 | 0.9831 | 0.0 | 0.8868 | 0.7703 | 0.9249 | 0.7271 | 0.9967 | 0.9449 | 0.0 | 0.8392 | 0.6450 | 0.8101 | 0.6320 | 0.9945 | 0.9324 |
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+ | 0.3036 | 5.12 | 420 | 0.2356 | 0.6869 | 0.7514 | 0.9303 | 0.9889 | 0.0 | 0.9184 | 0.6697 | 0.9145 | 0.7701 | 0.9983 | 0.9289 | 0.0 | 0.8442 | 0.6064 | 0.8075 | 0.6280 | 0.9932 | 0.9277 |
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+ | 0.1948 | 5.37 | 440 | 0.2138 | 0.6963 | 0.7661 | 0.9333 | 0.9801 | 0.0 | 0.8889 | 0.8187 | 0.8823 | 0.7944 | 0.9982 | 0.9443 | 0.0 | 0.8363 | 0.6453 | 0.8165 | 0.6381 | 0.9939 | 0.9330 |
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+ | 0.5839 | 5.61 | 460 | 0.2248 | 0.6998 | 0.7699 | 0.9350 | 0.9913 | 0.0 | 0.9310 | 0.7673 | 0.8695 | 0.8340 | 0.9966 | 0.9330 | 0.0 | 0.8511 | 0.6645 | 0.8143 | 0.6420 | 0.9936 | 0.9338 |
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+ | 0.2039 | 5.85 | 480 | 0.2311 | 0.6949 | 0.7695 | 0.9300 | 0.9801 | 0.0 | 0.8510 | 0.8609 | 0.8619 | 0.8362 | 0.9967 | 0.9417 | 0.0 | 0.8106 | 0.6365 | 0.8076 | 0.6740 | 0.9942 | 0.9304 |
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+ | 0.1818 | 6.1 | 500 | 0.2297 | 0.7037 | 0.7794 | 0.9338 | 0.9788 | 0.0 | 0.9420 | 0.8915 | 0.8103 | 0.8374 | 0.9955 | 0.9417 | 0.0 | 0.8651 | 0.6649 | 0.7871 | 0.6730 | 0.9942 | 0.9338 |
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+ | 0.1637 | 6.34 | 520 | 0.1984 | 0.7105 | 0.7757 | 0.9389 | 0.9817 | 0.0 | 0.9503 | 0.8314 | 0.8634 | 0.8062 | 0.9969 | 0.9409 | 0.0 | 0.8601 | 0.6899 | 0.8123 | 0.6756 | 0.9946 | 0.9380 |
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+ | 0.2034 | 6.59 | 540 | 0.1970 | 0.7170 | 0.7771 | 0.9426 | 0.9875 | 0.0 | 0.8953 | 0.8225 | 0.9205 | 0.8200 | 0.9939 | 0.9478 | 0.0 | 0.8602 | 0.7085 | 0.8317 | 0.6781 | 0.9926 | 0.9418 |
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+ | 0.2673 | 6.83 | 560 | 0.2055 | 0.7111 | 0.7800 | 0.9400 | 0.9907 | 0.0 | 0.9323 | 0.8022 | 0.8739 | 0.8651 | 0.9957 | 0.9366 | 0.0 | 0.8675 | 0.6941 | 0.8262 | 0.6591 | 0.9939 | 0.9392 |
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+ | 0.2446 | 7.07 | 580 | 0.2391 | 0.6769 | 0.7454 | 0.9286 | 0.9829 | 0.0 | 0.9409 | 0.9085 | 0.8298 | 0.5581 | 0.9979 | 0.9531 | 0.0 | 0.8470 | 0.6501 | 0.7880 | 0.5054 | 0.9944 | 0.9274 |
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+ | 0.1747 | 7.32 | 600 | 0.2450 | 0.6993 | 0.7642 | 0.9351 | 0.9872 | 0.0 | 0.9200 | 0.7115 | 0.9121 | 0.8223 | 0.9967 | 0.9458 | 0.0 | 0.8690 | 0.6249 | 0.8060 | 0.6552 | 0.9939 | 0.9335 |
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+ | 0.1262 | 7.56 | 620 | 0.2595 | 0.6955 | 0.7652 | 0.9315 | 0.9878 | 0.0 | 0.9453 | 0.7199 | 0.8657 | 0.8406 | 0.9970 | 0.9399 | 0.0 | 0.8588 | 0.6152 | 0.7856 | 0.6745 | 0.9946 | 0.9298 |
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+ | 0.1223 | 7.8 | 640 | 0.2334 | 0.7028 | 0.7704 | 0.9339 | 0.9861 | 0.0 | 0.9214 | 0.8069 | 0.8572 | 0.8234 | 0.9977 | 0.9527 | 0.0 | 0.8672 | 0.6354 | 0.7841 | 0.6854 | 0.9948 | 0.9334 |
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+ | 0.0915 | 8.05 | 660 | 0.2561 | 0.6879 | 0.7660 | 0.9258 | 0.9905 | 0.0 | 0.8363 | 0.8988 | 0.8221 | 0.8176 | 0.9967 | 0.9483 | 0.0 | 0.8053 | 0.6239 | 0.7819 | 0.6615 | 0.9946 | 0.9266 |
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+ | 0.1095 | 8.29 | 680 | 0.2018 | 0.7179 | 0.7813 | 0.9431 | 0.9851 | 0.0 | 0.9306 | 0.7844 | 0.9086 | 0.8650 | 0.9950 | 0.9425 | 0.0 | 0.8742 | 0.6973 | 0.8350 | 0.6831 | 0.9933 | 0.9421 |
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+ | 0.196 | 8.54 | 700 | 0.2125 | 0.7115 | 0.7834 | 0.9392 | 0.9800 | 0.0 | 0.9376 | 0.8437 | 0.8523 | 0.8722 | 0.9979 | 0.9515 | 0.0 | 0.8737 | 0.6855 | 0.8046 | 0.6707 | 0.9947 | 0.9389 |
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+ | 0.1548 | 8.78 | 720 | 0.1893 | 0.7261 | 0.7833 | 0.9467 | 0.9830 | 0.0 | 0.9370 | 0.8452 | 0.9035 | 0.8170 | 0.9973 | 0.9545 | 0.0 | 0.8776 | 0.7329 | 0.8299 | 0.6936 | 0.9944 | 0.9458 |
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+ | 0.1376 | 9.02 | 740 | 0.2467 | 0.7101 | 0.7706 | 0.9394 | 0.9868 | 0.0 | 0.9268 | 0.7615 | 0.9067 | 0.8150 | 0.9977 | 0.9536 | 0.0 | 0.8685 | 0.6624 | 0.8077 | 0.6831 | 0.9952 | 0.9381 |
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+ | 0.1569 | 9.27 | 760 | 0.2038 | 0.7184 | 0.7823 | 0.9432 | 0.9904 | 0.0 | 0.9064 | 0.8209 | 0.8961 | 0.8651 | 0.9976 | 0.9505 | 0.0 | 0.8672 | 0.7048 | 0.8265 | 0.6845 | 0.9952 | 0.9425 |
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+ | 0.1633 | 9.51 | 780 | 0.1953 | 0.7213 | 0.7820 | 0.9443 | 0.9882 | 0.0 | 0.9093 | 0.8436 | 0.8986 | 0.8371 | 0.9975 | 0.9499 | 0.0 | 0.8690 | 0.7129 | 0.8304 | 0.6919 | 0.9950 | 0.9436 |
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+ | 0.1501 | 9.76 | 800 | 0.2605 | 0.7013 | 0.7688 | 0.9352 | 0.9799 | 0.0 | 0.8636 | 0.8899 | 0.8816 | 0.7689 | 0.9975 | 0.9573 | 0.0 | 0.8129 | 0.6623 | 0.8220 | 0.6595 | 0.9950 | 0.9353 |
109
+ | 0.2376 | 10.0 | 820 | 0.2071 | 0.7186 | 0.7850 | 0.9438 | 0.9922 | 0.0 | 0.9306 | 0.8279 | 0.8795 | 0.8683 | 0.9962 | 0.9505 | 0.0 | 0.8731 | 0.7145 | 0.8282 | 0.6695 | 0.9947 | 0.9431 |
110
+ | 0.0856 | 10.24 | 840 | 0.2190 | 0.7113 | 0.7689 | 0.9410 | 0.9878 | 0.0 | 0.9119 | 0.8175 | 0.9127 | 0.7559 | 0.9964 | 0.9583 | 0.0 | 0.8737 | 0.6801 | 0.8152 | 0.6568 | 0.9948 | 0.9401 |
111
+ | 0.2257 | 10.49 | 860 | 0.2133 | 0.7124 | 0.7847 | 0.9402 | 0.9871 | 0.0 | 0.9328 | 0.8304 | 0.8554 | 0.8895 | 0.9980 | 0.9565 | 0.0 | 0.8557 | 0.7005 | 0.8108 | 0.6686 | 0.9948 | 0.9398 |
112
+ | 0.2022 | 10.73 | 880 | 0.2517 | 0.7030 | 0.7746 | 0.9355 | 0.9845 | 0.0 | 0.8451 | 0.8379 | 0.8945 | 0.8631 | 0.9973 | 0.9514 | 0.0 | 0.8109 | 0.6656 | 0.8229 | 0.6754 | 0.9949 | 0.9353 |
113
+ | 0.0923 | 10.98 | 900 | 0.2118 | 0.7122 | 0.7717 | 0.9416 | 0.9863 | 0.0 | 0.8904 | 0.8660 | 0.9057 | 0.7555 | 0.9979 | 0.9563 | 0.0 | 0.8418 | 0.7016 | 0.8320 | 0.6582 | 0.9952 | 0.9409 |
114
+ | 0.1639 | 11.22 | 920 | 0.1961 | 0.7244 | 0.7817 | 0.9471 | 0.9835 | 0.0 | 0.9354 | 0.8432 | 0.9118 | 0.8013 | 0.9967 | 0.9584 | 0.0 | 0.8649 | 0.7337 | 0.8397 | 0.6790 | 0.9951 | 0.9462 |
115
+ | 0.0676 | 11.46 | 940 | 0.2238 | 0.7135 | 0.7829 | 0.9421 | 0.9885 | 0.0 | 0.9482 | 0.7915 | 0.8765 | 0.8784 | 0.9973 | 0.9503 | 0.0 | 0.8653 | 0.7000 | 0.8282 | 0.6555 | 0.9952 | 0.9413 |
116
+ | 0.056 | 11.71 | 960 | 0.1908 | 0.7284 | 0.7848 | 0.9478 | 0.9862 | 0.0 | 0.9206 | 0.8434 | 0.9143 | 0.8319 | 0.9973 | 0.9583 | 0.0 | 0.8724 | 0.7308 | 0.8375 | 0.7048 | 0.9948 | 0.9470 |
117
+ | 0.1205 | 11.95 | 980 | 0.2000 | 0.7248 | 0.7914 | 0.9457 | 0.9844 | 0.0 | 0.9377 | 0.8256 | 0.8807 | 0.9132 | 0.9986 | 0.9581 | 0.0 | 0.8681 | 0.7249 | 0.8265 | 0.7016 | 0.9947 | 0.9450 |
118
+ | 0.0429 | 12.2 | 1000 | 0.1937 | 0.7285 | 0.7865 | 0.9476 | 0.9856 | 0.0 | 0.9298 | 0.8327 | 0.9064 | 0.8529 | 0.9982 | 0.9590 | 0.0 | 0.8687 | 0.7326 | 0.8335 | 0.7108 | 0.9952 | 0.9468 |
119
+ | 0.0992 | 12.44 | 1020 | 0.2081 | 0.7225 | 0.7858 | 0.9448 | 0.9863 | 0.0 | 0.9394 | 0.8478 | 0.8780 | 0.8522 | 0.9972 | 0.9576 | 0.0 | 0.8668 | 0.7191 | 0.8230 | 0.6955 | 0.9953 | 0.9441 |
120
+ | 0.089 | 12.68 | 1040 | 0.2044 | 0.7243 | 0.7812 | 0.9466 | 0.9832 | 0.0 | 0.9240 | 0.8403 | 0.9128 | 0.8095 | 0.9987 | 0.9561 | 0.0 | 0.8724 | 0.7274 | 0.8351 | 0.6844 | 0.9949 | 0.9457 |
121
+ | 0.1252 | 12.93 | 1060 | 0.2146 | 0.7138 | 0.7750 | 0.9417 | 0.9824 | 0.0 | 0.9125 | 0.8768 | 0.8895 | 0.7662 | 0.9973 | 0.9573 | 0.0 | 0.8521 | 0.6993 | 0.8267 | 0.6659 | 0.9950 | 0.9412 |
122
+ | 0.3201 | 13.17 | 1080 | 0.2169 | 0.7197 | 0.7849 | 0.9439 | 0.9796 | 0.0 | 0.9489 | 0.8613 | 0.8713 | 0.8352 | 0.9977 | 0.9580 | 0.0 | 0.8636 | 0.7225 | 0.8209 | 0.6774 | 0.9955 | 0.9434 |
123
+ | 0.101 | 13.41 | 1100 | 0.2023 | 0.7268 | 0.7848 | 0.9482 | 0.9871 | 0.0 | 0.9314 | 0.8350 | 0.9111 | 0.8315 | 0.9978 | 0.9587 | 0.0 | 0.8702 | 0.7416 | 0.8412 | 0.6806 | 0.9952 | 0.9473 |
124
+ | 0.0522 | 13.66 | 1120 | 0.2116 | 0.7207 | 0.7864 | 0.9441 | 0.9915 | 0.0 | 0.9083 | 0.8596 | 0.8771 | 0.8701 | 0.9983 | 0.9580 | 0.0 | 0.8691 | 0.7108 | 0.8236 | 0.6878 | 0.9957 | 0.9437 |
125
+
126
+
127
+ ### Framework versions
128
+
129
+ - Transformers 4.38.1
130
+ - Pytorch 2.1.2
131
+ - Datasets 2.18.0
132
+ - Tokenizers 0.15.2
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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