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End of training

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README.md CHANGED
@@ -20,10 +20,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [pczarnik/herbert-base-ner](https://huggingface.co/pczarnik/herbert-base-ner) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0018
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- - Precision: 0.9969
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- - Recall: 0.9975
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- - F1: 0.9972
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  ## Model description
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@@ -49,18 +49,19 @@ The following hyperparameters were used during training:
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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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  - lr_scheduler_warmup_steps: 327
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- - num_epochs: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
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  |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|
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- | 0.0064 | 0.7634 | 500 | 0.0046 | 0.9748 | 0.9910 | 0.9828 |
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- | 0.0027 | 1.5267 | 1000 | 0.0023 | 0.9895 | 0.9972 | 0.9933 |
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- | 0.0013 | 2.2901 | 1500 | 0.0027 | 0.9949 | 0.9970 | 0.9960 |
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- | 0.0013 | 3.0534 | 2000 | 0.0020 | 0.9965 | 0.9975 | 0.9970 |
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- | 0.0008 | 3.8168 | 2500 | 0.0020 | 0.9966 | 0.9975 | 0.9970 |
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- | 0.0005 | 4.5802 | 3000 | 0.0018 | 0.9969 | 0.9975 | 0.9972 |
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [pczarnik/herbert-base-ner](https://huggingface.co/pczarnik/herbert-base-ner) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0014
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+ - Precision: 0.9977
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+ - Recall: 0.9982
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+ - F1: 0.9980
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  ## Model description
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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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  - lr_scheduler_warmup_steps: 327
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+ - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
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  |:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|
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+ | 0.0072 | 0.7634 | 500 | 0.0061 | 0.9697 | 0.9905 | 0.98 |
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+ | 0.0034 | 1.5267 | 1000 | 0.0031 | 0.9879 | 0.9960 | 0.9920 |
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+ | 0.0016 | 2.2901 | 1500 | 0.0017 | 0.9958 | 0.9980 | 0.9969 |
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+ | 0.0012 | 3.0534 | 2000 | 0.0017 | 0.9958 | 0.9975 | 0.9966 |
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+ | 0.0007 | 3.8168 | 2500 | 0.0015 | 0.9976 | 0.9983 | 0.9980 |
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+ | 0.0004 | 4.5802 | 3000 | 0.0015 | 0.9976 | 0.9977 | 0.9977 |
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+ | 0.0006 | 5.3435 | 3500 | 0.0014 | 0.9977 | 0.9982 | 0.9980 |
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  ### Framework versions
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