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Bert-NER

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  1. README.md +20 -15
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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: 0.9994470908472269
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  - name: Recall
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  type: recall
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- value: 0.9994045846978268
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  - name: F1
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  type: f1
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- value: 0.9994258373205741
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  - name: Accuracy
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  type: accuracy
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- value: 0.9998240191819092
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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 [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ner dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0015
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- - Precision: 0.9994
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- - Recall: 0.9994
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- - F1: 0.9994
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- - Accuracy: 0.9998
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  ## Model description
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@@ -73,17 +73,22 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 5
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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- | 0.1477 | 1.0 | 626 | 0.0548 | 0.9686 | 0.9604 | 0.9644 | 0.9887 |
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- | 0.0571 | 2.0 | 1252 | 0.0249 | 0.9833 | 0.9820 | 0.9827 | 0.9949 |
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- | 0.037 | 3.0 | 1878 | 0.0075 | 0.9962 | 0.9953 | 0.9957 | 0.9987 |
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- | 0.0101 | 4.0 | 2504 | 0.0027 | 0.9987 | 0.9984 | 0.9986 | 0.9996 |
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- | 0.004 | 5.0 | 3130 | 0.0015 | 0.9994 | 0.9994 | 0.9994 | 0.9998 |
 
 
 
 
 
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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: 1.0
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  - name: Recall
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  type: recall
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+ value: 0.999957470335559
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  - name: F1
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  type: f1
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+ value: 0.9999787347155767
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9999890011988694
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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 ner dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0000
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+ - Precision: 1.0
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+ - Recall: 1.0000
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+ - F1: 1.0000
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+ - Accuracy: 1.0000
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  ## Model description
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
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+ | 0.0127 | 1.0 | 626 | 0.0069 | 0.9955 | 0.9957 | 0.9956 | 0.9986 |
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+ | 0.01 | 2.0 | 1252 | 0.0068 | 0.9972 | 0.9971 | 0.9972 | 0.9991 |
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+ | 0.0075 | 3.0 | 1878 | 0.0029 | 0.9987 | 0.9982 | 0.9984 | 0.9995 |
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+ | 0.006 | 4.0 | 2504 | 0.0010 | 0.9994 | 0.9994 | 0.9994 | 0.9998 |
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+ | 0.0052 | 5.0 | 3130 | 0.0007 | 0.9997 | 0.9997 | 0.9997 | 0.9999 |
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+ | 0.0032 | 6.0 | 3756 | 0.0003 | 0.9999 | 0.9998 | 0.9999 | 1.0000 |
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+ | 0.003 | 7.0 | 4382 | 0.0001 | 1.0000 | 1.0000 | 1.0000 | 1.0000 |
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+ | 0.0013 | 8.0 | 5008 | 0.0000 | 1.0 | 1.0 | 1.0 | 1.0 |
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+ | 0.0013 | 9.0 | 5634 | 0.0001 | 1.0000 | 0.9999 | 0.9999 | 1.0000 |
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+ | 0.0011 | 10.0 | 6260 | 0.0000 | 1.0 | 1.0000 | 1.0000 | 1.0000 |
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  ### Framework versions