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dtorber
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bert-base-cased

Text Classification
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Safetensors
bert
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  2. runs/Apr14_20-30-24_tardis/events.out.tfevents.1713119459.tardis.126941.0 +2 -2
  3. runs/Apr14_20-30-24_tardis/events.out.tfevents.1713120503.tardis.126941.1 +3 -0
README.md CHANGED
@@ -18,14 +18,14 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3280
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- - F1 Macro: 0.9125
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- - F1: 0.9421
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- - F1 Neg: 0.8830
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- - Acc: 0.9225
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- - Prec: 0.9474
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- - Recall: 0.9368
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- - Mcc: 0.8252
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  ## Model description
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@@ -51,16 +51,18 @@ The following hyperparameters were used during training:
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  - distributed_type: multi-GPU
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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: 3
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 | F1 Neg | Acc | Prec | Recall | Mcc |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:------:|:------:|
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- | 0.2495 | 1.0 | 1647 | 0.3554 | 0.9111 | 0.9349 | 0.8874 | 0.9175 | 0.9442 | 0.9258 | 0.8226 |
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- | 0.091 | 2.0 | 3294 | 0.5621 | 0.9011 | 0.9308 | 0.8714 | 0.91 | 0.9167 | 0.9453 | 0.8031 |
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- | 0.0202 | 3.0 | 4941 | 0.6612 | 0.9049 | 0.9318 | 0.8780 | 0.9125 | 0.9300 | 0.9336 | 0.8098 |
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6472
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+ - F1 Macro: 0.9202
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+ - F1: 0.9481
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+ - F1 Neg: 0.8923
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+ - Acc: 0.93
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+ - Prec: 0.9446
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+ - Recall: 0.9517
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+ - Mcc: 0.8405
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  ## Model description
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  - distributed_type: multi-GPU
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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: 5
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  - mixed_precision_training: Native AMP
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 | F1 Neg | Acc | Prec | Recall | Mcc |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------:|:------:|:------:|:------:|
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+ | 0.227 | 1.0 | 1647 | 0.4468 | 0.8956 | 0.9225 | 0.8687 | 0.9025 | 0.9393 | 0.9062 | 0.7922 |
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+ | 0.0771 | 2.0 | 3294 | 0.7307 | 0.8813 | 0.9216 | 0.8409 | 0.895 | 0.8821 | 0.9648 | 0.7706 |
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+ | 0.0192 | 3.0 | 4941 | 0.7794 | 0.8969 | 0.9258 | 0.8681 | 0.905 | 0.9258 | 0.9258 | 0.7938 |
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+ | 0.011 | 4.0 | 6588 | 0.9531 | 0.8893 | 0.9194 | 0.8591 | 0.8975 | 0.9249 | 0.9141 | 0.7787 |
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+ | 0.0028 | 5.0 | 8235 | 1.0420 | 0.8927 | 0.9206 | 0.8649 | 0.9 | 0.9355 | 0.9062 | 0.7863 |
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  ### Framework versions
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