BaselMousi commited on
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End of training

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README.md CHANGED
@@ -26,16 +26,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.9257130223303117
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  - name: Recall
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  type: recall
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- value: 0.9367938248126189
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  - name: F1
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  type: f1
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- value: 0.9312204614956908
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  - name: Accuracy
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  type: accuracy
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- value: 0.9835734824534926
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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
@@ -45,11 +45,11 @@ 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: 0.0620
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- - Precision: 0.9257
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- - Recall: 0.9368
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- - F1: 0.9312
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- - Accuracy: 0.9836
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  ## Model description
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@@ -80,9 +80,9 @@ 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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- | 0.2523 | 1.0 | 878 | 0.0704 | 0.9004 | 0.9198 | 0.9100 | 0.9796 |
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- | 0.0523 | 2.0 | 1756 | 0.0620 | 0.9208 | 0.9346 | 0.9276 | 0.9829 |
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- | 0.0304 | 3.0 | 2634 | 0.0620 | 0.9257 | 0.9368 | 0.9312 | 0.9836 |
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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: 0.9274864175629227
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  - name: Recall
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  type: recall
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+ value: 0.9357870007830854
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  - name: F1
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  type: f1
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+ value: 0.931618220291792
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9837005734983398
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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: 0.0618
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+ - Precision: 0.9275
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+ - Recall: 0.9358
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+ - F1: 0.9316
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+ - Accuracy: 0.9837
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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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+ | 0.2391 | 1.0 | 878 | 0.0698 | 0.8993 | 0.9191 | 0.9091 | 0.9797 |
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+ | 0.0529 | 2.0 | 1756 | 0.0609 | 0.92 | 0.9340 | 0.9269 | 0.9829 |
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+ | 0.0304 | 3.0 | 2634 | 0.0618 | 0.9275 | 0.9358 | 0.9316 | 0.9837 |
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  ### Framework versions
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