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End of training

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README.md CHANGED
@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.8794544654641443
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  - name: Recall
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  type: recall
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- value: 0.8945072155722117
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  - name: F1
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  type: f1
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- value: 0.8869169763185625
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  - name: Accuracy
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  type: accuracy
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- value: 0.9731996759178356
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1902
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- - Precision: 0.8795
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- - Recall: 0.8945
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- - F1: 0.8869
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- - Accuracy: 0.9732
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  ## Model description
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@@ -79,10 +79,10 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | No log | 1.0 | 439 | 0.1942 | 0.8818 | 0.8931 | 0.8874 | 0.9732 |
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- | 0.0009 | 2.0 | 878 | 0.1902 | 0.8817 | 0.8933 | 0.8875 | 0.9729 |
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- | 0.001 | 3.0 | 1317 | 0.1894 | 0.8794 | 0.8952 | 0.8872 | 0.9733 |
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- | 0.0009 | 4.0 | 1756 | 0.1902 | 0.8795 | 0.8945 | 0.8869 | 0.9732 |
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  ### Framework versions
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9253070709306186
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  - name: Recall
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  type: recall
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+ value: 0.9354513927732409
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  - name: F1
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  type: f1
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+ value: 0.9303515798842902
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9834305050280394
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0619
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+ - Precision: 0.9253
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+ - Recall: 0.9355
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+ - F1: 0.9304
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+ - Accuracy: 0.9834
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | No log | 1.0 | 439 | 0.0877 | 0.8779 | 0.8955 | 0.8866 | 0.9754 |
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+ | 0.2182 | 2.0 | 878 | 0.0626 | 0.9193 | 0.9299 | 0.9245 | 0.9820 |
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+ | 0.0557 | 3.0 | 1317 | 0.0612 | 0.9252 | 0.9323 | 0.9287 | 0.9829 |
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+ | 0.0346 | 4.0 | 1756 | 0.0619 | 0.9253 | 0.9355 | 0.9304 | 0.9834 |
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  ### Framework versions
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