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--- |
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license: mit |
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base_model: Amna100/PreTraining-MLM |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: fold_3 |
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results: [] |
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--- |
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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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[<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/10xzvwgi) |
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# fold_3 |
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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: 0.0125 |
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- Precision: 0.6887 |
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- Recall: 0.6744 |
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- F1: 0.6814 |
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- Accuracy: 0.9992 |
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- Roc Auc: 0.9939 |
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- Pr Auc: 0.9998 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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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: 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 | Precision | Recall | F1 | Accuracy | Roc Auc | Pr Auc | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:------:| |
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| 0.0286 | 1.0 | 632 | 0.0159 | 0.5908 | 0.5035 | 0.5436 | 0.9988 | 0.9931 | 0.9998 | |
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| 0.0115 | 2.0 | 1264 | 0.0125 | 0.6887 | 0.6744 | 0.6814 | 0.9992 | 0.9939 | 0.9998 | |
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| 0.0079 | 3.0 | 1896 | 0.0170 | 0.8419 | 0.5289 | 0.6496 | 0.9992 | 0.9859 | 0.9995 | |
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| 0.0035 | 4.0 | 2528 | 0.0150 | 0.7146 | 0.7344 | 0.7244 | 0.9992 | 0.9903 | 0.9997 | |
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| 0.002 | 5.0 | 3160 | 0.0166 | 0.6471 | 0.7621 | 0.6999 | 0.9991 | 0.9917 | 0.9997 | |
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| 0.0017 | 6.0 | 3792 | 0.0196 | 0.8300 | 0.6651 | 0.7385 | 0.9993 | 0.9865 | 0.9995 | |
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| 0.0012 | 7.0 | 4424 | 0.0175 | 0.7143 | 0.7621 | 0.7374 | 0.9993 | 0.9920 | 0.9997 | |
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| 0.0004 | 8.0 | 5056 | 0.0176 | 0.7262 | 0.7413 | 0.7337 | 0.9992 | 0.9920 | 0.9997 | |
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### Framework versions |
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- Transformers 4.41.0.dev0 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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