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Amna100
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fold_2

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  1. README.md +14 -21
  2. model.safetensors +1 -1
README.md CHANGED
@@ -16,22 +16,19 @@ model-index:
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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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  should probably proofread and complete it, then remove this comment. -->
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- [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/wbresearch/FineTuning-ADE-DropOUT/runs/rw6sjeap)
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- [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/wbresearch/FineTuning-ADE-DropOUT/runs/g4pyaj7k)
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- [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/wbresearch/FineTuning-ADE-DropOUT/runs/t4il24wd)
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- [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/wbresearch/FineTuning-ADE-DropOUT/runs/qf2ywrxq)
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- [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/wbresearch/FineTuning-ADE-DropOUT/runs/9xmjfnoc)
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- [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/wbresearch/FineTuning-ADE-DropOUT/runs/vp363qmp)
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  # fold_2
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  This model is a fine-tuned version of [Amna100/PreTraining-MLM](https://huggingface.co/Amna100/PreTraining-MLM) on the None dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0100
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- - Precision: 0.7190
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- - Recall: 0.7
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- - F1: 0.7094
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- - Accuracy: 0.9995
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- - Roc Auc: 0.9959
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  - Pr Auc: 0.9999
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Roc Auc | Pr Auc |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:------:|
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- | 0.026 | 1.0 | 632 | 0.0136 | 0.4503 | 0.7059 | 0.5498 | 0.9989 | 0.9956 | 0.9999 |
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- | 0.0109 | 2.0 | 1264 | 0.0104 | 0.7270 | 0.6029 | 0.6592 | 0.9994 | 0.9951 | 0.9999 |
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- | 0.0063 | 3.0 | 1896 | 0.0100 | 0.7190 | 0.7 | 0.7094 | 0.9995 | 0.9959 | 0.9999 |
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- | 0.0027 | 4.0 | 2528 | 0.0113 | 0.6872 | 0.7235 | 0.7049 | 0.9994 | 0.9942 | 0.9999 |
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- | 0.0012 | 5.0 | 3160 | 0.0145 | 0.8194 | 0.5471 | 0.6561 | 0.9994 | 0.9924 | 0.9999 |
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- | 0.0015 | 6.0 | 3792 | 0.0131 | 0.6952 | 0.7176 | 0.7062 | 0.9994 | 0.9935 | 0.9999 |
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- | 0.0008 | 7.0 | 4424 | 0.0139 | 0.7604 | 0.7 | 0.7289 | 0.9995 | 0.9925 | 0.9999 |
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- | 0.0002 | 8.0 | 5056 | 0.0163 | 0.7418 | 0.6676 | 0.7028 | 0.9994 | 0.9907 | 0.9998 |
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- | 0.0001 | 9.0 | 5688 | 0.0163 | 0.6551 | 0.7206 | 0.6863 | 0.9994 | 0.9927 | 0.9999 |
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  ### Framework versions
 
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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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  should probably proofread and complete it, then remove this comment. -->
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+ [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/lvieenf2)
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+ [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/fgis28rc)
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+ [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/9tw0vsla)
 
 
 
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  # fold_2
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  This model is a fine-tuned version of [Amna100/PreTraining-MLM](https://huggingface.co/Amna100/PreTraining-MLM) on the None dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0108
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+ - Precision: 0.6774
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+ - Recall: 0.616
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+ - F1: 0.6453
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+ - Accuracy: 0.9992
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+ - Roc Auc: 0.9952
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  - Pr Auc: 0.9999
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  ## Model description
 
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  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Roc Auc | Pr Auc |
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  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:------:|
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+ | 0.0322 | 1.0 | 632 | 0.0118 | 0.6869 | 0.544 | 0.6071 | 0.9992 | 0.9956 | 0.9999 |
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+ | 0.0115 | 2.0 | 1264 | 0.0108 | 0.6774 | 0.616 | 0.6453 | 0.9992 | 0.9952 | 0.9999 |
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+ | 0.0071 | 3.0 | 1896 | 0.0115 | 0.6253 | 0.7387 | 0.6773 | 0.9992 | 0.9960 | 0.9999 |
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+ | 0.0028 | 4.0 | 2528 | 0.0134 | 0.7723 | 0.624 | 0.6903 | 0.9994 | 0.9943 | 0.9999 |
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+ | 0.0015 | 5.0 | 3160 | 0.0137 | 0.7240 | 0.7413 | 0.7325 | 0.9994 | 0.9938 | 0.9998 |
 
 
 
 
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  ### Framework versions
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