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End of training

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README.md CHANGED
@@ -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: 0.9337843833185449
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  - name: Recall
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  type: recall
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- value: 0.9418279449602864
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  - name: F1
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  type: f1
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- value: 0.9377889167362852
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  - name: Accuracy
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  type: accuracy
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- value: 0.9845584380510588
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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 conll2003 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0955
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- - Precision: 0.9338
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- - Recall: 0.9418
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- - F1: 0.9378
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- - Accuracy: 0.9846
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  ## Model description
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@@ -67,26 +67,22 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 3e-05
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  - train_batch_size: 32
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  - eval_batch_size: 32
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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: 8
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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 | 439 | 0.0978 | 0.9270 | 0.9286 | 0.9278 | 0.9825 |
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- | 0.0068 | 2.0 | 878 | 0.0887 | 0.9255 | 0.9409 | 0.9332 | 0.9838 |
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- | 0.0058 | 3.0 | 1317 | 0.0874 | 0.9348 | 0.9401 | 0.9375 | 0.9844 |
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- | 0.0045 | 4.0 | 1756 | 0.0927 | 0.9341 | 0.9399 | 0.9370 | 0.9842 |
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- | 0.003 | 5.0 | 2195 | 0.0911 | 0.9346 | 0.9417 | 0.9381 | 0.9845 |
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- | 0.0021 | 6.0 | 2634 | 0.0927 | 0.9336 | 0.9431 | 0.9383 | 0.9847 |
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- | 0.0013 | 7.0 | 3073 | 0.0939 | 0.9321 | 0.9432 | 0.9376 | 0.9845 |
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- | 0.0011 | 8.0 | 3512 | 0.0955 | 0.9338 | 0.9418 | 0.9378 | 0.9846 |
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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: 0.8794544654641443
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  - name: Recall
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  type: recall
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+ value: 0.8945072155722117
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  - name: F1
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  type: f1
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+ value: 0.8869169763185625
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9731996759178356
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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 conll2003 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1902
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+ - Precision: 0.8795
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+ - Recall: 0.8945
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+ - F1: 0.8869
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+ - Accuracy: 0.9732
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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: 32
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  - eval_batch_size: 32
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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: 4
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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 | 439 | 0.1942 | 0.8818 | 0.8931 | 0.8874 | 0.9732 |
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+ | 0.0009 | 2.0 | 878 | 0.1902 | 0.8817 | 0.8933 | 0.8875 | 0.9729 |
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+ | 0.001 | 3.0 | 1317 | 0.1894 | 0.8794 | 0.8952 | 0.8872 | 0.9733 |
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+ | 0.0009 | 4.0 | 1756 | 0.1902 | 0.8795 | 0.8945 | 0.8869 | 0.9732 |
 
 
 
 
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  ### Framework versions
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