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Bert-NER

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End of training

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  1. README.md +17 -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: 0.9985643067910454
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  - name: Recall
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  type: recall
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- value: 0.998333826213025
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  - name: F1
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  type: f1
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- value: 0.9984490532010821
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  - name: Accuracy
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  type: accuracy
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- value: 0.9986316806181584
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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: 0.0014
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- - Precision: 0.9986
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- - Recall: 0.9983
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- - F1: 0.9984
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- - Accuracy: 0.9986
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  ## Model description
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@@ -73,14 +73,17 @@ 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: 2
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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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- | 0.0316 | 1.0 | 7904 | 0.0056 | 0.9940 | 0.9931 | 0.9935 | 0.9945 |
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- | 0.0102 | 2.0 | 15808 | 0.0014 | 0.9986 | 0.9983 | 0.9984 | 0.9986 |
 
 
 
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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: 0.9994470908472269
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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
 
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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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  - 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