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---
license: mit
base_model: Amna100/PreTraining-MLM
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fold_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<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)
[<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)
[<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)
[<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)
[<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)
[<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)
[<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)
# fold_3
This model is a fine-tuned version of [Amna100/PreTraining-MLM](https://huggingface.co/Amna100/PreTraining-MLM) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0125
- Precision: 0.6887
- Recall: 0.6744
- F1: 0.6814
- Accuracy: 0.9992
- Roc Auc: 0.9939
- Pr Auc: 0.9998
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | Roc Auc | Pr Auc |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:------:|
| 0.0286 | 1.0 | 632 | 0.0159 | 0.5908 | 0.5035 | 0.5436 | 0.9988 | 0.9931 | 0.9998 |
| 0.0115 | 2.0 | 1264 | 0.0125 | 0.6887 | 0.6744 | 0.6814 | 0.9992 | 0.9939 | 0.9998 |
| 0.0079 | 3.0 | 1896 | 0.0170 | 0.8419 | 0.5289 | 0.6496 | 0.9992 | 0.9859 | 0.9995 |
| 0.0035 | 4.0 | 2528 | 0.0150 | 0.7146 | 0.7344 | 0.7244 | 0.9992 | 0.9903 | 0.9997 |
| 0.002 | 5.0 | 3160 | 0.0166 | 0.6471 | 0.7621 | 0.6999 | 0.9991 | 0.9917 | 0.9997 |
| 0.0017 | 6.0 | 3792 | 0.0196 | 0.8300 | 0.6651 | 0.7385 | 0.9993 | 0.9865 | 0.9995 |
| 0.0012 | 7.0 | 4424 | 0.0175 | 0.7143 | 0.7621 | 0.7374 | 0.9993 | 0.9920 | 0.9997 |
| 0.0004 | 8.0 | 5056 | 0.0176 | 0.7262 | 0.7413 | 0.7337 | 0.9992 | 0.9920 | 0.9997 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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