---
license: mit
base_model: Amna100/PreTraining-MLM
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fold_2
  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)
# fold_2

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: 0.0100
- Precision: 0.7190
- Recall: 0.7
- F1: 0.7094
- Accuracy: 0.9995
- Roc Auc: 0.9959
- Pr Auc: 0.9999

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:------:|
| 0.026         | 1.0   | 632  | 0.0136          | 0.4503    | 0.7059 | 0.5498 | 0.9989   | 0.9956  | 0.9999 |
| 0.0109        | 2.0   | 1264 | 0.0104          | 0.7270    | 0.6029 | 0.6592 | 0.9994   | 0.9951  | 0.9999 |
| 0.0063        | 3.0   | 1896 | 0.0100          | 0.7190    | 0.7    | 0.7094 | 0.9995   | 0.9959  | 0.9999 |
| 0.0027        | 4.0   | 2528 | 0.0113          | 0.6872    | 0.7235 | 0.7049 | 0.9994   | 0.9942  | 0.9999 |
| 0.0012        | 5.0   | 3160 | 0.0145          | 0.8194    | 0.5471 | 0.6561 | 0.9994   | 0.9924  | 0.9999 |
| 0.0015        | 6.0   | 3792 | 0.0131          | 0.6952    | 0.7176 | 0.7062 | 0.9994   | 0.9935  | 0.9999 |
| 0.0008        | 7.0   | 4424 | 0.0139          | 0.7604    | 0.7    | 0.7289 | 0.9995   | 0.9925  | 0.9999 |
| 0.0002        | 8.0   | 5056 | 0.0163          | 0.7418    | 0.6676 | 0.7028 | 0.9994   | 0.9907  | 0.9998 |
| 0.0001        | 9.0   | 5688 | 0.0163          | 0.6551    | 0.7206 | 0.6863 | 0.9994   | 0.9927  | 0.9999 |


### Framework versions

- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1