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End of training

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  1. README.md +10 -10
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@@ -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 [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3626
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- - Precision: 0.8862
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- - Recall: 0.8829
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- - F1: 0.8845
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  ## Model description
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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: 8
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- - eval_batch_size: 8
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  - seed: 42
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  - gradient_accumulation_steps: 2
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- - total_train_batch_size: 16
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine
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  - lr_scheduler_warmup_ratio: 0.1
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
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  |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|
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- | 0.2839 | 0.9995 | 1024 | 0.2913 | 0.8693 | 0.8853 | 0.8663 |
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- | 0.2686 | 2.0 | 2049 | 0.2956 | 0.8914 | 0.8804 | 0.8849 |
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- | 0.2786 | 2.9985 | 3072 | 0.3626 | 0.8862 | 0.8829 | 0.8845 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3184
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+ - Precision: 0.8894
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+ - Recall: 0.8897
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+ - F1: 0.8894
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  ## Model description
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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: 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: cosine
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  - lr_scheduler_warmup_ratio: 0.1
 
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
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  |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|
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+ | 0.3301 | 0.9990 | 512 | 0.3780 | 0.8509 | 0.8697 | 0.8514 |
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+ | 0.3103 | 2.0 | 1025 | 0.2916 | 0.8907 | 0.8848 | 0.8870 |
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+ | 0.1607 | 2.9971 | 1536 | 0.3184 | 0.8894 | 0.8897 | 0.8894 |
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  ### Framework versions