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Training complete

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  1. README.md +16 -12
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0330
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- - Precision: 0.9467
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- - Recall: 0.9645
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- - F1: 0.9555
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- - Accuracy: 0.9903
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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: 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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- | 0.0464 | 1.0 | 2180 | 0.0397 | 0.9267 | 0.9565 | 0.9413 | 0.9875 |
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- | 0.0263 | 2.0 | 4360 | 0.0340 | 0.9400 | 0.9608 | 0.9503 | 0.9894 |
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- | 0.0156 | 3.0 | 6540 | 0.0330 | 0.9467 | 0.9645 | 0.9555 | 0.9903 |
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0517
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+ - Precision: 0.9468
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+ - Recall: 0.9623
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+ - F1: 0.9545
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+ - Accuracy: 0.9908
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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: 7e-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: 7
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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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+ | 0.047 | 1.0 | 2180 | 0.0385 | 0.9149 | 0.9466 | 0.9305 | 0.9879 |
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+ | 0.0308 | 2.0 | 4360 | 0.0350 | 0.9181 | 0.9521 | 0.9348 | 0.9887 |
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+ | 0.0199 | 3.0 | 6540 | 0.0334 | 0.9333 | 0.9546 | 0.9438 | 0.9895 |
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+ | 0.0112 | 4.0 | 8720 | 0.0377 | 0.9412 | 0.9598 | 0.9504 | 0.9901 |
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+ | 0.0065 | 5.0 | 10900 | 0.0417 | 0.9373 | 0.9619 | 0.9495 | 0.9903 |
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+ | 0.0033 | 6.0 | 13080 | 0.0445 | 0.9464 | 0.9611 | 0.9537 | 0.9907 |
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+ | 0.0014 | 7.0 | 15260 | 0.0517 | 0.9468 | 0.9623 | 0.9545 | 0.9908 |
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  ### Framework versions