---
license: mit
base_model: Amna100/PreTraining-MLM
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fold_1
  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)
# fold_1

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: 0.0113
- Precision: 0.7155
- Recall: 0.7233
- F1: 0.7194
- Accuracy: 0.9993
- Roc Auc: 0.9952
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:------:|
| 0.0241        | 1.0   | 632  | 0.0123          | 0.6317    | 0.6427 | 0.6371 | 0.9991   | 0.9958  | 0.9999 |
| 0.0098        | 2.0   | 1264 | 0.0114          | 0.8436    | 0.6580 | 0.7393 | 0.9994   | 0.9946  | 0.9998 |
| 0.0062        | 3.0   | 1896 | 0.0113          | 0.7155    | 0.7233 | 0.7194 | 0.9993   | 0.9952  | 0.9999 |
| 0.0021        | 4.0   | 2528 | 0.0132          | 0.7990    | 0.6841 | 0.7371 | 0.9993   | 0.9944  | 0.9998 |
| 0.0013        | 5.0   | 3160 | 0.0145          | 0.7783    | 0.7037 | 0.7391 | 0.9993   | 0.9956  | 0.9998 |
| 0.001         | 6.0   | 3792 | 0.0159          | 0.7730    | 0.7495 | 0.7611 | 0.9994   | 0.9958  | 0.9998 |
| 0.0004        | 7.0   | 4424 | 0.0143          | 0.7104    | 0.7908 | 0.7485 | 0.9993   | 0.9964  | 0.9999 |
| 0.0002        | 8.0   | 5056 | 0.0173          | 0.8005    | 0.7255 | 0.7611 | 0.9994   | 0.9951  | 0.9999 |
| 0.0002        | 9.0   | 5688 | 0.0176          | 0.7981    | 0.7233 | 0.7589 | 0.9993   | 0.9948  | 0.9999 |


### Framework versions

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