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Bert-NER

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End of training

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  1. README.md +14 -14
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@@ -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: 0.9998842940781709
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  - name: Recall
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  type: recall
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- value: 0.9998380192062941
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  - name: F1
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  type: f1
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- value: 0.9998611561068173
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  - name: Accuracy
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  type: accuracy
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- value: 0.999938944347773
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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 [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the ner dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0001
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- - Precision: 0.9999
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- - Recall: 0.9998
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- - F1: 0.9999
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- - Accuracy: 0.9999
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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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- | 0.0342 | 1.0 | 688 | 0.0063 | 0.9950 | 0.9917 | 0.9934 | 0.9956 |
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- | 0.0117 | 2.0 | 1376 | 0.0015 | 0.9979 | 0.9974 | 0.9977 | 0.9988 |
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- | 0.0049 | 3.0 | 2064 | 0.0006 | 0.9991 | 0.9994 | 0.9992 | 0.9995 |
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- | 0.0017 | 4.0 | 2752 | 0.0001 | 0.9997 | 0.9997 | 0.9997 | 0.9999 |
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- | 0.001 | 5.0 | 3440 | 0.0001 | 0.9999 | 0.9998 | 0.9999 | 0.9999 |
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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: 0.9950245302230862
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  - name: Recall
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  type: recall
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+ value: 0.9949324324324325
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  - name: F1
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  type: f1
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+ value: 0.9949784791965567
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9976269686240996
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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-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the ner dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0019
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+ - Precision: 0.9950
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+ - Recall: 0.9949
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+ - F1: 0.9950
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+ - Accuracy: 0.9976
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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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+ | 0.0679 | 1.0 | 688 | 0.0131 | 0.9998 | 0.9826 | 0.9911 | 0.9920 |
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+ | 0.0331 | 2.0 | 1376 | 0.0085 | 0.9998 | 0.9826 | 0.9911 | 0.9921 |
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+ | 0.0248 | 3.0 | 2064 | 0.0057 | 0.9999 | 0.9826 | 0.9912 | 0.9921 |
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+ | 0.0155 | 4.0 | 2752 | 0.0026 | 0.9948 | 0.9936 | 0.9942 | 0.9969 |
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+ | 0.0103 | 5.0 | 3440 | 0.0019 | 0.9950 | 0.9949 | 0.9950 | 0.9976 |
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  ### Framework versions