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End of training

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  1. README.md +16 -13
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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: 0.5583832335329342
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  - name: Recall
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  type: recall
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- value: 0.520949720670391
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  - name: F1
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  type: f1
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- value: 0.5390173410404625
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  - name: Accuracy
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  type: accuracy
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- value: 0.9721157973387596
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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 indian_names dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1028
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- - Precision: 0.5584
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- - Recall: 0.5209
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- - F1: 0.5390
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- - Accuracy: 0.9721
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 2e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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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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- | No log | 1.0 | 213 | 0.1348 | 0.4605 | 0.3613 | 0.4049 | 0.9633 |
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- | No log | 2.0 | 426 | 0.1028 | 0.5584 | 0.5209 | 0.5390 | 0.9721 |
 
 
 
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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: 0.9805194805194806
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  - name: Recall
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  type: recall
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+ value: 0.984171322160149
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  - name: F1
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  type: f1
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+ value: 0.9823420074349444
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9989348679713209
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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 indian_names dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0053
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+ - Precision: 0.9805
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+ - Recall: 0.9842
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+ - F1: 0.9823
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+ - Accuracy: 0.9989
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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  - train_batch_size: 16
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  - eval_batch_size: 16
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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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+ | No log | 1.0 | 213 | 0.0572 | 0.6793 | 0.7356 | 0.7063 | 0.9820 |
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+ | No log | 2.0 | 426 | 0.0248 | 0.8912 | 0.8887 | 0.8900 | 0.9936 |
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+ | 0.0713 | 3.0 | 639 | 0.0118 | 0.9570 | 0.9534 | 0.9552 | 0.9973 |
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+ | 0.0713 | 4.0 | 852 | 0.0067 | 0.9777 | 0.9800 | 0.9788 | 0.9987 |
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+ | 0.0164 | 5.0 | 1065 | 0.0053 | 0.9805 | 0.9842 | 0.9823 | 0.9989 |
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  ### Framework versions