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Michal0607
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herbert-finetuned-datetime

Token Classification
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bert
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  1. README.md +13 -12
  2. model.safetensors +1 -1
  3. runs/Apr25_22-19-23_laptop-Micha.local/events.out.tfevents.1745612364.laptop-Micha.local.41583.2 +3 -0
  4. training_args.bin +1 -1
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: 0.0005
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- - Precision: 0.9998
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- - Recall: 0.9998
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- - F1: 0.9998
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  ## Model description
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@@ -48,19 +48,20 @@ The following hyperparameters were used during training:
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  - seed: 42
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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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- - num_epochs: 7
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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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- | No log | 1.0 | 81 | 0.0015 | 0.9998 | 0.9998 | 0.9998 |
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- | No log | 2.0 | 162 | 0.0007 | 0.9999 | 0.9999 | 0.9999 |
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- | No log | 3.0 | 243 | 0.0005 | 0.9999 | 0.9999 | 0.9999 |
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- | 0.0319 | 4.0 | 324 | 0.0006 | 0.9999 | 0.9999 | 0.9999 |
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- | 0.0319 | 5.0 | 405 | 0.0005 | 0.9999 | 0.9999 | 0.9999 |
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- | 0.0319 | 6.0 | 486 | 0.0005 | 0.9999 | 0.9999 | 0.9999 |
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- | 0.0319 | 7.0 | 567 | 0.0005 | 0.9998 | 0.9998 | 0.9998 |
 
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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: 0.0006
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+ - Precision: 0.9999
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+ - Recall: 0.9999
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+ - F1: 0.9999
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  ## Model description
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  - seed: 42
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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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+ - num_epochs: 8
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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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+ | No log | 1.0 | 81 | 0.0013 | 0.9998 | 0.9998 | 0.9998 |
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+ | No log | 2.0 | 162 | 0.0006 | 0.9999 | 0.9999 | 0.9999 |
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+ | No log | 3.0 | 243 | 0.0006 | 0.9999 | 0.9999 | 0.9999 |
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+ | 0.0225 | 4.0 | 324 | 0.0006 | 0.9999 | 0.9999 | 0.9999 |
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+ | 0.0225 | 5.0 | 405 | 0.0006 | 0.9999 | 0.9999 | 0.9999 |
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+ | 0.0225 | 6.0 | 486 | 0.0005 | 0.9999 | 0.9999 | 0.9999 |
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+ | 0.0225 | 7.0 | 567 | 0.0005 | 0.9999 | 0.9999 | 0.9999 |
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+ | 0.0002 | 8.0 | 648 | 0.0006 | 0.9999 | 0.9999 | 0.9999 |
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  ### Framework versions
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