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bert-base-uncased-finetuned-classification

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update model card README.md

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@@ -16,11 +16,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 2.0911
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- - Mse: 2.0911
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- - Mae: 1.1038
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- - R2: 0.7130
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- - Accuracy: 0.4757
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  ## Model description
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@@ -45,22 +45,17 @@ The following hyperparameters were used during training:
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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 | Mse | Mae | R2 | Accuracy |
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- |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:--------:|
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- | 1.4606 | 1.0 | 1897 | 2.4983 | 2.4983 | 1.2018 | 0.6571 | 0.3745 |
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- | 0.8186 | 2.0 | 3794 | 2.2750 | 2.2750 | 1.1567 | 0.6877 | 0.4441 |
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- | 0.6563 | 3.0 | 5691 | 2.0911 | 2.0911 | 1.1038 | 0.7130 | 0.4757 |
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- | 0.4981 | 4.0 | 7588 | 2.1422 | 2.1422 | 1.1253 | 0.7060 | 0.4631 |
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- | 0.4029 | 5.0 | 9485 | 2.1098 | 2.1098 | 1.1153 | 0.7104 | 0.4335 |
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- | 0.3188 | 6.0 | 11382 | 2.2132 | 2.2132 | 1.1358 | 0.6962 | 0.4219 |
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- | 0.2744 | 7.0 | 13279 | 2.3058 | 2.3058 | 1.1678 | 0.6835 | 0.4188 |
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- | 0.237 | 8.0 | 15176 | 2.2705 | 2.2705 | 1.1474 | 0.6884 | 0.4230 |
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- | 0.2193 | 9.0 | 17073 | 2.2546 | 2.2546 | 1.1515 | 0.6906 | 0.4251 |
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- | 0.1927 | 10.0 | 18970 | 2.2537 | 2.2537 | 1.1516 | 0.6907 | 0.4124 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 55.2339
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+ - Mse: 55.2339
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+ - Mae: 5.3678
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+ - R2: 0.6879
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+ - Accuracy: 0.1888
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  ## Model description
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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: 5
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:--------:|
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+ | 21.1293 | 1.0 | 1897 | 61.8443 | 61.8443 | 5.6934 | 0.6506 | 0.1624 |
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+ | 16.6699 | 2.0 | 3794 | 55.2339 | 55.2339 | 5.3678 | 0.6879 | 0.1888 |
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+ | 14.8768 | 3.0 | 5691 | 54.4710 | 54.4710 | 5.3302 | 0.6923 | 0.1888 |
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+ | 11.4835 | 4.0 | 7588 | 54.4710 | 54.4710 | 5.3302 | 0.6923 | 0.1888 |
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+ | 13.9168 | 5.0 | 9485 | 54.4710 | 54.4710 | 5.3302 | 0.6923 | 0.1888 |
 
 
 
 
 
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  ### Framework versions