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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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  metrics:
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  - precision
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  - recall
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  - accuracy
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  model-index:
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  - name: bert-finetuned-ner-ime
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- results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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  # bert-finetuned-ner-ime
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- This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.4592
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- - Precision: 0.6456
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- - Recall: 0.3813
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- - F1: 0.4794
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- - Accuracy: 0.6108
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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 | 221 | 2.6044 | 0.6263 | 0.3774 | 0.4710 | 0.6081 |
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- | No log | 2.0 | 442 | 2.5040 | 0.6286 | 0.3848 | 0.4774 | 0.6100 |
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- | 2.7612 | 3.0 | 663 | 2.4592 | 0.6456 | 0.3813 | 0.4794 | 0.6108 |
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  ### Framework versions
 
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  ---
 
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  tags:
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  - generated_from_trainer
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+ datasets:
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+ - conll2003
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  metrics:
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  - precision
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  - recall
 
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  - accuracy
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  model-index:
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  - name: bert-finetuned-ner-ime
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+ results:
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: conll2003
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+ type: conll2003
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+ config: conll2003
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+ split: train
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+ args: conll2003
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.998195331607817
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+ - name: Recall
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+ type: recall
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+ value: 0.9982190349544073
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+ - name: F1
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+ type: f1
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+ value: 0.9982071831403979
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.9979751132733664
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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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  # bert-finetuned-ner-ime
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+ This model is a fine-tuned version of [snunlp/KR-BERT-char16424](https://huggingface.co/snunlp/KR-BERT-char16424) on the conll2003 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0076
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+ - Precision: 0.9982
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+ - Recall: 0.9982
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+ - F1: 0.9982
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+ - Accuracy: 0.9980
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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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+ | 0.0378 | 1.0 | 1756 | 0.0290 | 0.9934 | 0.9939 | 0.9936 | 0.9920 |
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+ | 0.0214 | 2.0 | 3512 | 0.0138 | 0.9969 | 0.9970 | 0.9970 | 0.9965 |
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+ | 0.0151 | 3.0 | 5268 | 0.0076 | 0.9982 | 0.9982 | 0.9982 | 0.9980 |
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  ### Framework versions