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End of training

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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 [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.1015
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- - Accuracy: 0.9693
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- - F1: 0.8766
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- - Precision: 0.8803
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- - Recall: 0.8729
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  ## Model description
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@@ -43,30 +43,28 @@ 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: 5
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  - eval_batch_size: 5
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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: 12
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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- | No log | 1.0 | 100 | 0.3499 | 0.875 | 0.0 | 0.0 | 0.0 |
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- | No log | 2.0 | 200 | 0.2517 | 0.9068 | 0.4211 | 0.9412 | 0.2712 |
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- | No log | 3.0 | 300 | 0.1835 | 0.9396 | 0.7077 | 0.8961 | 0.5847 |
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- | No log | 4.0 | 400 | 0.1338 | 0.9587 | 0.8219 | 0.8911 | 0.7627 |
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- | 0.2507 | 5.0 | 500 | 0.1043 | 0.9640 | 0.8522 | 0.875 | 0.8305 |
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- | 0.2507 | 6.0 | 600 | 0.1076 | 0.9629 | 0.8472 | 0.8739 | 0.8220 |
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- | 0.2507 | 7.0 | 700 | 0.1061 | 0.9619 | 0.8475 | 0.8475 | 0.8475 |
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- | 0.2507 | 8.0 | 800 | 0.1015 | 0.9693 | 0.8766 | 0.8803 | 0.8729 |
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- | 0.2507 | 9.0 | 900 | 0.1099 | 0.9650 | 0.8596 | 0.8632 | 0.8559 |
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- | 0.0434 | 10.0 | 1000 | 0.1101 | 0.9661 | 0.8632 | 0.8707 | 0.8559 |
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- | 0.0434 | 11.0 | 1100 | 0.1054 | 0.9693 | 0.8766 | 0.8803 | 0.8729 |
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- | 0.0434 | 12.0 | 1200 | 0.1066 | 0.9682 | 0.8729 | 0.8729 | 0.8729 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1653
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+ - Accuracy: 0.9454
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+ - F1: 0.7754
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+ - Precision: 0.7975
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+ - Recall: 0.7545
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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: 5
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  - eval_batch_size: 5
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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: 10
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  ### Training results
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
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+ | No log | 1.0 | 90 | 0.3559 | 0.875 | 0.0 | 0.0 | 0.0 |
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+ | No log | 2.0 | 180 | 0.2851 | 0.9087 | 0.4602 | 0.8814 | 0.3114 |
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+ | No log | 3.0 | 270 | 0.2462 | 0.9049 | 0.4940 | 0.7381 | 0.3713 |
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+ | No log | 4.0 | 360 | 0.2183 | 0.9222 | 0.6232 | 0.7890 | 0.5150 |
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+ | No log | 5.0 | 450 | 0.1938 | 0.9304 | 0.6869 | 0.7846 | 0.6108 |
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+ | 0.2617 | 6.0 | 540 | 0.1804 | 0.9349 | 0.7129 | 0.7941 | 0.6467 |
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+ | 0.2617 | 7.0 | 630 | 0.1752 | 0.9364 | 0.7231 | 0.7929 | 0.6647 |
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+ | 0.2617 | 8.0 | 720 | 0.1719 | 0.9409 | 0.7539 | 0.7857 | 0.7246 |
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+ | 0.2617 | 9.0 | 810 | 0.1676 | 0.9424 | 0.7601 | 0.7922 | 0.7305 |
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+ | 0.2617 | 10.0 | 900 | 0.1653 | 0.9454 | 0.7754 | 0.7975 | 0.7545 |
 
 
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  ### Framework versions
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