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End of training

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README.md CHANGED
@@ -1,7 +1,7 @@
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  ---
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  library_name: transformers
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  license: apache-2.0
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- base_model: distilbert-base-uncased
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  tags:
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  - generated_from_trainer
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  datasets:
@@ -26,16 +26,16 @@ model-index:
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  metrics:
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  - name: Precision
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  type: precision
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- value: 0.559040590405904
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  - name: Recall
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  type: recall
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- value: 0.2808155699721965
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  - name: F1
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  type: f1
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- value: 0.3738433066008636
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  - name: Accuracy
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  type: accuracy
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- value: 0.9409601983668933
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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
@@ -43,13 +43,13 @@ should probably proofread and complete it, then remove this comment. -->
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  # distilbert_wnut_model
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- This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2784
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- - Precision: 0.5590
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- - Recall: 0.2808
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- - F1: 0.3738
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- - Accuracy: 0.9410
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  ## Model description
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@@ -74,14 +74,18 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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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 | 0.2856 | 0.4622 | 0.2150 | 0.2935 | 0.9368 |
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- | No log | 2.0 | 426 | 0.2784 | 0.5590 | 0.2808 | 0.3738 | 0.9410 |
 
 
 
 
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  ### Framework versions
 
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  ---
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  library_name: transformers
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  license: apache-2.0
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+ base_model: distilbert/distilbert-base-uncased
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  tags:
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  - generated_from_trainer
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  datasets:
 
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  metrics:
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  - name: Precision
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  type: precision
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+ value: 0.5218476903870163
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  - name: Recall
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  type: recall
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+ value: 0.3873957367933272
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  - name: F1
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  type: f1
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+ value: 0.4446808510638298
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  - name: Accuracy
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  type: accuracy
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+ value: 0.946346885554273
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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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  # distilbert_wnut_model
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+ This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the wnut_17 dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.3052
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+ - Precision: 0.5218
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+ - Recall: 0.3874
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+ - F1: 0.4447
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+ - Accuracy: 0.9463
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  ## Model description
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  - seed: 42
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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+ - num_epochs: 6
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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 | 0.2801 | 0.5586 | 0.2428 | 0.3385 | 0.9384 |
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+ | No log | 2.0 | 426 | 0.2573 | 0.5228 | 0.2975 | 0.3792 | 0.9425 |
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+ | 0.1769 | 3.0 | 639 | 0.2859 | 0.5510 | 0.3253 | 0.4091 | 0.9450 |
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+ | 0.1769 | 4.0 | 852 | 0.2965 | 0.5499 | 0.3522 | 0.4294 | 0.9462 |
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+ | 0.0496 | 5.0 | 1065 | 0.2951 | 0.5123 | 0.3846 | 0.4394 | 0.9458 |
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+ | 0.0496 | 6.0 | 1278 | 0.3052 | 0.5218 | 0.3874 | 0.4447 | 0.9463 |
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  ### Framework versions
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