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bert-finetuned-ner

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update model card README.md

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  ---
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  license: apache-2.0
 
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  tags:
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  - generated_from_trainer
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  datasets:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.9377799900447984
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  - name: Recall
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  type: recall
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- value: 0.9511948838774823
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  - name: F1
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  type: f1
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- value: 0.9444398028239619
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  - name: Accuracy
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  type: accuracy
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- value: 0.9862689115205746
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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
@@ -43,10 +44,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0610
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- - Precision: 0.9378
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- - Recall: 0.9512
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- - F1: 0.9444
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  - Accuracy: 0.9863
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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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- | 0.0874 | 1.0 | 1756 | 0.0679 | 0.9211 | 0.9329 | 0.9269 | 0.9822 |
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- | 0.0329 | 2.0 | 3512 | 0.0620 | 0.9372 | 0.9520 | 0.9446 | 0.9868 |
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- | 0.0184 | 3.0 | 5268 | 0.0610 | 0.9378 | 0.9512 | 0.9444 | 0.9863 |
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  ### Framework versions
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- - Transformers 4.29.2
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  - Pytorch 2.0.1+cu118
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- - Datasets 2.12.0
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  - Tokenizers 0.13.3
 
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  ---
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  license: apache-2.0
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+ base_model: bert-base-cased
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  tags:
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  - generated_from_trainer
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  datasets:
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9314955430835259
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  - name: Recall
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  type: recall
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+ value: 0.9496802423426456
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  - name: F1
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  type: f1
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+ value: 0.9404999999999999
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9862836286572084
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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 [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0581
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+ - Precision: 0.9315
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+ - Recall: 0.9497
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+ - F1: 0.9405
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  - Accuracy: 0.9863
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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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+ | 0.0768 | 1.0 | 1756 | 0.0701 | 0.9052 | 0.9332 | 0.9190 | 0.9804 |
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+ | 0.04 | 2.0 | 3512 | 0.0557 | 0.9277 | 0.9483 | 0.9379 | 0.9862 |
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+ | 0.0258 | 3.0 | 5268 | 0.0581 | 0.9315 | 0.9497 | 0.9405 | 0.9863 |
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  ### Framework versions
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+ - Transformers 4.31.0
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  - Pytorch 2.0.1+cu118
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+ - Datasets 2.14.4
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  - Tokenizers 0.13.3