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procit011/anger_frustration_detection_9

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  1. README.md +14 -10
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -19,10 +19,10 @@ 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: 0.0825
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- - F1: 0.9840
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- - Accuracy: 0.9881
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- - F1 Yes: 0.976
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  ## Model description
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@@ -50,17 +50,21 @@ The following hyperparameters were used during training:
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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: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | F1 Yes |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:------:|
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- | 0.2779 | 1.0 | 567 | 0.1380 | 0.9687 | 0.9770 | 0.9525 |
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- | 0.3814 | 2.0 | 1134 | 0.1375 | 0.9740 | 0.9810 | 0.9606 |
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- | 0.2305 | 3.0 | 1701 | 0.1152 | 0.9820 | 0.9868 | 0.9728 |
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- | 0.1595 | 4.0 | 2268 | 0.1057 | 0.9839 | 0.9881 | 0.9756 |
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- | 0.1194 | 5.0 | 2835 | 0.1177 | 0.9832 | 0.9876 | 0.9746 |
 
 
 
 
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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: 0.0961
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+ - F1: 0.9870
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+ - Accuracy: 0.9903
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+ - F1 Yes: 0.9805
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  ## Model description
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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 | F1 Yes |
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  |:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|:------:|
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+ | 0.4447 | 1.0 | 567 | 0.1354 | 0.9645 | 0.9740 | 0.9461 |
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+ | 0.5017 | 2.0 | 1134 | 0.1364 | 0.9709 | 0.9788 | 0.9558 |
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+ | 0.3631 | 3.0 | 1701 | 0.0939 | 0.9821 | 0.9868 | 0.9730 |
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+ | 0.1957 | 4.0 | 2268 | 0.1017 | 0.9827 | 0.9872 | 0.9739 |
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+ | 0.2480 | 5.0 | 2835 | 0.1281 | 0.9832 | 0.9876 | 0.9746 |
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+ | 0.0166 | 6.0 | 3402 | 0.1206 | 0.9851 | 0.9890 | 0.9775 |
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+ | 0.0452 | 7.0 | 3969 | 0.1049 | 0.9881 | 0.9912 | 0.9821 |
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+ | 0.0261 | 8.0 | 4536 | 0.1177 | 0.9857 | 0.9894 | 0.9784 |
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+ | 0.0004 | 9.0 | 5103 | 0.1145 | 0.9869 | 0.9903 | 0.9803 |
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  ### Framework versions
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