---
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/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/lvieenf2)
[<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)
[<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)
[<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/ccjl3n87)
# 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: 0.0107
- Precision: 0.7923
- Recall: 0.6297
- F1: 0.7017
- Accuracy: 0.9993
- Roc Auc: 0.9951
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:------:|
| 0.0275        | 1.0   | 632  | 0.0138          | 0.6206    | 0.4976 | 0.5524 | 0.9991   | 0.9926  | 0.9998 |
| 0.0096        | 2.0   | 1264 | 0.0136          | 0.5756    | 0.7358 | 0.6460 | 0.9991   | 0.9958  | 0.9998 |
| 0.006         | 3.0   | 1896 | 0.0107          | 0.7923    | 0.6297 | 0.7017 | 0.9993   | 0.9951  | 0.9999 |
| 0.0023        | 4.0   | 2528 | 0.0137          | 0.7813    | 0.6910 | 0.7334 | 0.9994   | 0.9898  | 0.9998 |
| 0.0011        | 5.0   | 3160 | 0.0141          | 0.7978    | 0.6887 | 0.7392 | 0.9994   | 0.9943  | 0.9999 |
| 0.001         | 6.0   | 3792 | 0.0159          | 0.7812    | 0.7075 | 0.7426 | 0.9993   | 0.9865  | 0.9998 |


### Framework versions

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