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anger_frustration_detection_7_train

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  1. README.md +14 -11
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -19,9 +19,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0472
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- - F1: 0.9916
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- - Accuracy: 0.9939
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 5e-05
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- - train_batch_size: 8
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  - eval_batch_size: 8
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  - seed: 42
 
 
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  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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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- - lr_scheduler_warmup_steps: 50
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- - num_epochs: 3
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
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- | 0.0838 | 1.0 | 2116 | 0.0788 | 0.9843 | 0.9887 |
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- | 0.0006 | 2.0 | 4232 | 0.0704 | 0.9836 | 0.9882 |
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- | 0.0001 | 3.0 | 6348 | 0.0552 | 0.9883 | 0.9915 |
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0796
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+ - F1: 0.9827
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+ - Accuracy: 0.9869
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 4
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  - eval_batch_size: 8
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  - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 16
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  - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_steps: 0.1
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+ - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
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+ | 0.5180 | 1.0 | 1107 | 0.0904 | 0.9782 | 0.9837 |
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+ | 0.0220 | 2.0 | 2214 | 0.0530 | 0.9892 | 0.9919 |
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+ | 0.1185 | 3.0 | 3321 | 0.0673 | 0.9880 | 0.9910 |
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+ | 0.0001 | 4.0 | 4428 | 0.0633 | 0.9886 | 0.9914 |
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  ### Framework versions
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