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End of training

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README.md CHANGED
@@ -18,9 +18,9 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.2129
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- - Accuracy: 0.94
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- - F1: 0.9394
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  ## Model description
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@@ -51,31 +51,31 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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- | No log | 0.01 | 50 | 0.6913 | 0.55 | 0.3903 |
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- | No log | 0.02 | 100 | 0.6909 | 0.59 | 0.5186 |
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- | No log | 0.03 | 150 | 0.6934 | 0.45 | 0.2793 |
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- | No log | 0.04 | 200 | 0.6889 | 0.57 | 0.5709 |
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- | No log | 0.05 | 250 | 0.6818 | 0.56 | 0.5607 |
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- | No log | 0.06 | 300 | 0.6854 | 0.56 | 0.5607 |
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- | No log | 0.07 | 350 | 0.6878 | 0.56 | 0.5607 |
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- | No log | 0.08 | 400 | 0.7014 | 0.56 | 0.5607 |
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- | No log | 0.09 | 450 | 0.6797 | 0.56 | 0.5607 |
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- | 0.6799 | 0.1 | 500 | 0.6731 | 0.56 | 0.5607 |
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- | 0.6799 | 0.11 | 550 | 0.6490 | 0.64 | 0.6203 |
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- | 0.6799 | 0.12 | 600 | 0.6456 | 0.71 | 0.7049 |
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- | 0.6799 | 0.13 | 650 | 0.6259 | 0.64 | 0.6203 |
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- | 0.6799 | 0.14 | 700 | 0.5264 | 0.83 | 0.8304 |
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- | 0.6799 | 0.15 | 750 | 0.4671 | 0.83 | 0.8304 |
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- | 0.6799 | 0.16 | 800 | 0.3387 | 0.94 | 0.9394 |
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- | 0.6799 | 0.17 | 850 | 0.2935 | 0.94 | 0.9394 |
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- | 0.6799 | 0.18 | 900 | 0.2604 | 0.94 | 0.9394 |
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- | 0.6799 | 0.19 | 950 | 0.2443 | 0.94 | 0.9394 |
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- | 0.4884 | 0.2 | 1000 | 0.2355 | 0.94 | 0.9394 |
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- | 0.4884 | 0.2 | 1050 | 0.2286 | 0.94 | 0.9394 |
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- | 0.4884 | 0.21 | 1100 | 0.2240 | 0.94 | 0.9394 |
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- | 0.4884 | 0.22 | 1150 | 0.2201 | 0.94 | 0.9394 |
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- | 0.4884 | 0.23 | 1200 | 0.2165 | 0.94 | 0.9394 |
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- | 0.4884 | 0.24 | 1250 | 0.2129 | 0.94 | 0.9394 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6515
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+ - Accuracy: 0.56
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+ - F1: 0.5579
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
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+ | No log | 0.0 | 50 | 0.6897 | 0.54 | 0.3787 |
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+ | No log | 0.01 | 100 | 0.6899 | 0.6 | 0.5926 |
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+ | No log | 0.01 | 150 | 0.6952 | 0.46 | 0.2899 |
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+ | No log | 0.01 | 200 | 0.6874 | 0.63 | 0.6194 |
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+ | No log | 0.02 | 250 | 0.6849 | 0.64 | 0.6092 |
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+ | No log | 0.02 | 300 | 0.6929 | 0.46 | 0.2899 |
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+ | No log | 0.02 | 350 | 0.6830 | 0.6 | 0.5390 |
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+ | No log | 0.03 | 400 | 0.6821 | 0.54 | 0.3787 |
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+ | No log | 0.03 | 450 | 0.6812 | 0.63 | 0.6095 |
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+ | 0.6924 | 0.03 | 500 | 0.6806 | 0.62 | 0.6077 |
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+ | 0.6924 | 0.04 | 550 | 0.6770 | 0.62 | 0.5969 |
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+ | 0.6924 | 0.04 | 600 | 0.6805 | 0.58 | 0.5746 |
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+ | 0.6924 | 0.04 | 650 | 0.6800 | 0.59 | 0.5857 |
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+ | 0.6924 | 0.05 | 700 | 0.6732 | 0.63 | 0.6008 |
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+ | 0.6924 | 0.05 | 750 | 0.6820 | 0.56 | 0.5387 |
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+ | 0.6924 | 0.05 | 800 | 0.6652 | 0.64 | 0.6253 |
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+ | 0.6924 | 0.06 | 850 | 0.6634 | 0.59 | 0.5896 |
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+ | 0.6924 | 0.06 | 900 | 0.6604 | 0.61 | 0.6103 |
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+ | 0.6924 | 0.06 | 950 | 0.6733 | 0.62 | 0.5936 |
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+ | 0.6842 | 0.07 | 1000 | 0.6590 | 0.65 | 0.6176 |
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+ | 0.6842 | 0.07 | 1050 | 0.6549 | 0.6 | 0.6005 |
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+ | 0.6842 | 0.07 | 1100 | 0.6521 | 0.63 | 0.6242 |
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+ | 0.6842 | 0.08 | 1150 | 0.6524 | 0.61 | 0.6015 |
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+ | 0.6842 | 0.08 | 1200 | 0.6587 | 0.57 | 0.5634 |
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+ | 0.6842 | 0.09 | 1250 | 0.6515 | 0.56 | 0.5579 |
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  ### Framework versions
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