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Bert-NER

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End of training

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  1. README.md +15 -13
  2. model.safetensors +1 -1
README.md CHANGED
@@ -20,21 +20,21 @@ model-index:
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  name: ner
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  type: ner
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  config: indian_names
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- split: train
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  args: indian_names
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.9948381144840311
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  - name: Recall
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  type: recall
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- value: 0.972891113354671
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  - name: F1
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  type: f1
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- value: 0.9837422213534031
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  - name: Accuracy
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  type: accuracy
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- value: 0.9932984044056051
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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-uncased](https://huggingface.co/bert-base-uncased) on the ner dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0270
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- - Precision: 0.9948
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- - Recall: 0.9729
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  - F1: 0.9837
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- - Accuracy: 0.9933
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  ## Model description
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@@ -73,15 +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: 3
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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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- | 0.0875 | 1.0 | 501 | 0.0328 | 0.9923 | 0.9696 | 0.9808 | 0.9920 |
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- | 0.0333 | 2.0 | 1002 | 0.0289 | 0.9935 | 0.9726 | 0.9830 | 0.9929 |
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- | 0.0283 | 3.0 | 1503 | 0.0270 | 0.9948 | 0.9729 | 0.9837 | 0.9933 |
 
 
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  ### Framework versions
 
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  name: ner
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  type: ner
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  config: indian_names
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+ split: test
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  args: indian_names
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.9752319346327347
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  - name: Recall
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  type: recall
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+ value: 0.9923783128356141
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  - name: F1
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  type: f1
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+ value: 0.9837304142519855
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9730393535444438
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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-uncased](https://huggingface.co/bert-base-uncased) on the ner dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1205
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+ - Precision: 0.9752
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+ - Recall: 0.9924
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  - F1: 0.9837
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+ - Accuracy: 0.9730
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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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+ | 0.0825 | 1.0 | 501 | 0.1031 | 0.9600 | 0.9917 | 0.9756 | 0.9770 |
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+ | 0.0337 | 2.0 | 1002 | 0.1491 | 0.9615 | 0.9942 | 0.9776 | 0.9648 |
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+ | 0.0285 | 3.0 | 1503 | 0.1169 | 0.9754 | 0.9913 | 0.9833 | 0.9723 |
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+ | 0.0249 | 4.0 | 2004 | 0.1054 | 0.9724 | 0.9921 | 0.9821 | 0.9783 |
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+ | 0.0232 | 5.0 | 2505 | 0.1205 | 0.9752 | 0.9924 | 0.9837 | 0.9730 |
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  ### Framework versions
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