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Robeczech-CERED2

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End of training

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  1. README.md +20 -20
  2. model.safetensors +1 -1
  3. runs/Mar31_16-42-38_dgx10/events.out.tfevents.1743432162.dgx10.924525.2 +3 -0
  4. runs/Mar31_16-42-38_dgx10/events.out.tfevents.1743446781.dgx10.924525.3 +3 -0
  5. training_args.bin +2 -2
README.md CHANGED
@@ -20,14 +20,14 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [ufal/robeczech-base](https://huggingface.co/ufal/robeczech-base) on the generator dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5511
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- - Accuracy: 0.8942
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- - Micro Precision: 0.8942
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- - Micro Recall: 0.8942
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- - Micro F1: 0.8942
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- - Macro Precision: 0.8789
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- - Macro Recall: 0.8525
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- - Macro F1: 0.8597
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  ## Model description
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@@ -46,14 +46,14 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 1e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
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  - gradient_accumulation_steps: 2
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  - total_train_batch_size: 32
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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: reduce_lr_on_plateau
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  - lr_scheduler_warmup_steps: 1000
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  - num_epochs: 10
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@@ -61,16 +61,16 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Micro Precision | Micro Recall | Micro F1 | Macro Precision | Macro Recall | Macro F1 |
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  |:-------------:|:------:|:------:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|
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- | 0.5022 | 1.0000 | 11305 | 0.4532 | 0.8668 | 0.8668 | 0.8668 | 0.8668 | 0.8227 | 0.7921 | 0.7968 |
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- | 0.3915 | 2.0 | 22611 | 0.3896 | 0.8853 | 0.8853 | 0.8853 | 0.8853 | 0.8454 | 0.8386 | 0.8359 |
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- | 0.2895 | 3.0000 | 33916 | 0.3825 | 0.8929 | 0.8929 | 0.8929 | 0.8929 | 0.8591 | 0.8409 | 0.8448 |
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- | 0.2404 | 4.0 | 45222 | 0.4012 | 0.8944 | 0.8944 | 0.8944 | 0.8944 | 0.8545 | 0.8517 | 0.8489 |
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- | 0.1901 | 5.0000 | 56527 | 0.4284 | 0.8988 | 0.8988 | 0.8988 | 0.8988 | 0.8640 | 0.8599 | 0.8577 |
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- | 0.1586 | 6.0 | 67833 | 0.4548 | 0.8983 | 0.8983 | 0.8983 | 0.8983 | 0.8518 | 0.8607 | 0.8526 |
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- | 0.1325 | 7.0000 | 79138 | 0.4821 | 0.9020 | 0.9020 | 0.9020 | 0.9020 | 0.8729 | 0.8619 | 0.8641 |
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- | 0.1124 | 8.0 | 90444 | 0.5333 | 0.8976 | 0.8976 | 0.8976 | 0.8976 | 0.8685 | 0.8518 | 0.8556 |
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- | 0.0883 | 9.0000 | 101749 | 0.6051 | 0.8977 | 0.8977 | 0.8977 | 0.8977 | 0.8615 | 0.8558 | 0.8551 |
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- | 0.0874 | 9.9996 | 113050 | 0.6492 | 0.9006 | 0.9006 | 0.9006 | 0.9006 | 0.8601 | 0.8637 | 0.8581 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [ufal/robeczech-base](https://huggingface.co/ufal/robeczech-base) on the generator dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.9004
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+ - Accuracy: 0.8819
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+ - Micro Precision: 0.8819
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+ - Micro Recall: 0.8819
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+ - Micro F1: 0.8819
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+ - Macro Precision: 0.8488
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+ - Macro Recall: 0.8326
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+ - Macro F1: 0.8379
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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: 0.0001
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  - train_batch_size: 16
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  - eval_batch_size: 16
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  - seed: 42
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  - gradient_accumulation_steps: 2
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  - total_train_batch_size: 32
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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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  - lr_scheduler_warmup_steps: 1000
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  - num_epochs: 10
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Micro Precision | Micro Recall | Micro F1 | Macro Precision | Macro Recall | Macro F1 |
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  |:-------------:|:------:|:------:|:---------------:|:--------:|:---------------:|:------------:|:--------:|:---------------:|:------------:|:--------:|
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+ | 0.5518 | 1.0000 | 11305 | 0.5227 | 0.8496 | 0.8496 | 0.8496 | 0.8496 | 0.8293 | 0.7648 | 0.7779 |
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+ | 0.4797 | 2.0 | 22611 | 0.4742 | 0.8623 | 0.8623 | 0.8623 | 0.8623 | 0.8191 | 0.8141 | 0.8052 |
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+ | 0.369 | 3.0000 | 33916 | 0.4886 | 0.8684 | 0.8684 | 0.8684 | 0.8684 | 0.8493 | 0.8094 | 0.8198 |
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+ | 0.308 | 4.0 | 45222 | 0.4829 | 0.8685 | 0.8685 | 0.8685 | 0.8685 | 0.8347 | 0.8231 | 0.8228 |
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+ | 0.2395 | 5.0000 | 56527 | 0.4928 | 0.8755 | 0.8755 | 0.8755 | 0.8755 | 0.8300 | 0.8326 | 0.8265 |
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+ | 0.1852 | 6.0 | 67833 | 0.5186 | 0.8799 | 0.8799 | 0.8799 | 0.8799 | 0.8528 | 0.8385 | 0.8401 |
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+ | 0.1353 | 7.0000 | 79138 | 0.5951 | 0.8809 | 0.8809 | 0.8809 | 0.8809 | 0.8419 | 0.8419 | 0.8377 |
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+ | 0.0945 | 8.0 | 90444 | 0.6848 | 0.8847 | 0.8847 | 0.8847 | 0.8847 | 0.8510 | 0.8478 | 0.8438 |
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+ | 0.0551 | 9.0000 | 101749 | 0.7723 | 0.8867 | 0.8867 | 0.8867 | 0.8867 | 0.8469 | 0.8440 | 0.8405 |
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+ | 0.0319 | 9.9996 | 113050 | 0.8430 | 0.8882 | 0.8882 | 0.8882 | 0.8882 | 0.8492 | 0.8487 | 0.8448 |
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  ### Framework versions
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