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End of training

Browse files
README.md CHANGED
@@ -6,9 +6,36 @@ tags:
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  - generated_from_trainer
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  datasets:
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  - wnut_17
 
 
 
 
 
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  model-index:
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  - name: my_awesome_wnut_model
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- results: []
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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
@@ -17,6 +44,12 @@ should probably proofread and complete it, then remove this comment. -->
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  # my_awesome_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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  ## Model description
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@@ -41,13 +74,15 @@ The following hyperparameters were used during training:
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  - seed: 42
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  - optimizer: Use 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: 0.01
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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 | 0.0141 | 3 | 2.4349 | 0.0045 | 0.0593 | 0.0083 | 0.1690 |
 
 
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  ### Framework versions
 
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  - generated_from_trainer
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  datasets:
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  - wnut_17
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+ metrics:
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+ - precision
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+ - recall
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+ - f1
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+ - accuracy
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  model-index:
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  - name: my_awesome_wnut_model
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+ results:
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+ - task:
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+ name: Token Classification
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+ type: token-classification
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+ dataset:
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+ name: wnut_17
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+ type: wnut_17
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+ config: wnut_17
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+ split: test
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+ args: wnut_17
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+ metrics:
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+ - name: Precision
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+ type: precision
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+ value: 0.5390279823269514
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+ - name: Recall
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+ type: recall
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+ value: 0.3392029657089898
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+ - name: F1
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+ type: f1
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+ value: 0.41638225255972694
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+ - name: Accuracy
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+ type: accuracy
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+ value: 0.943952802359882
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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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  # my_awesome_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: 0.2711
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+ - Precision: 0.5390
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+ - Recall: 0.3392
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+ - F1: 0.4164
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+ - Accuracy: 0.9440
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  ## Model description
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  - seed: 42
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  - optimizer: Use 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: 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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+ | No log | 1.0 | 213 | 0.2856 | 0.6172 | 0.2465 | 0.3523 | 0.9386 |
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+ | No log | 2.0 | 426 | 0.2706 | 0.5669 | 0.3142 | 0.4043 | 0.9422 |
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+ | 0.1857 | 3.0 | 639 | 0.2711 | 0.5390 | 0.3392 | 0.4164 | 0.9440 |
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  ### Framework versions
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