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update model card README.md

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@@ -24,16 +24,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.5527638190954773
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  - name: Recall
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  type: recall
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- value: 0.4077849860982391
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  - name: F1
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  type: f1
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- value: 0.46933333333333327
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  - name: Accuracy
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  type: accuracy
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- value: 0.9475439271514685
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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
@@ -43,11 +43,11 @@ 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 wnut_17 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.3030
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- - Precision: 0.5528
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- - Recall: 0.4078
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- - F1: 0.4693
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- - Accuracy: 0.9475
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  ## Model description
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@@ -78,11 +78,11 @@ 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 | 213 | 0.2577 | 0.5099 | 0.3568 | 0.4198 | 0.9437 |
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- | No log | 2.0 | 426 | 0.2675 | 0.5406 | 0.3948 | 0.4563 | 0.9463 |
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- | 0.0737 | 3.0 | 639 | 0.3040 | 0.5737 | 0.3716 | 0.4511 | 0.9465 |
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- | 0.0737 | 4.0 | 852 | 0.3042 | 0.5514 | 0.3976 | 0.4620 | 0.9474 |
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- | 0.0307 | 5.0 | 1065 | 0.3030 | 0.5528 | 0.4078 | 0.4693 | 0.9475 |
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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: 0.5552523874488404
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  - name: Recall
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  type: recall
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+ value: 0.37720111214087115
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  - name: F1
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  type: f1
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+ value: 0.44922737306843263
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9469454063528707
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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 wnut_17 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.2942
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+ - Precision: 0.5553
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+ - Recall: 0.3772
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+ - F1: 0.4492
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+ - Accuracy: 0.9469
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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 | 213 | 0.2666 | 0.6024 | 0.2808 | 0.3831 | 0.9405 |
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+ | No log | 2.0 | 426 | 0.2605 | 0.5708 | 0.3364 | 0.4233 | 0.9456 |
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+ | 0.1299 | 3.0 | 639 | 0.2827 | 0.5658 | 0.3346 | 0.4205 | 0.9452 |
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+ | 0.1299 | 4.0 | 852 | 0.2836 | 0.5503 | 0.3753 | 0.4463 | 0.9469 |
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+ | 0.051 | 5.0 | 1065 | 0.2942 | 0.5553 | 0.3772 | 0.4492 | 0.9469 |
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  ### Framework versions