---
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/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)
# 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: 0.0108
- Precision: 0.6774
- Recall: 0.616
- F1: 0.6453
- Accuracy: 0.9992
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:------:|
| 0.0322        | 1.0   | 632  | 0.0118          | 0.6869    | 0.544  | 0.6071 | 0.9992   | 0.9956  | 0.9999 |
| 0.0115        | 2.0   | 1264 | 0.0108          | 0.6774    | 0.616  | 0.6453 | 0.9992   | 0.9952  | 0.9999 |
| 0.0071        | 3.0   | 1896 | 0.0115          | 0.6253    | 0.7387 | 0.6773 | 0.9992   | 0.9960  | 0.9999 |
| 0.0028        | 4.0   | 2528 | 0.0134          | 0.7723    | 0.624  | 0.6903 | 0.9994   | 0.9943  | 0.9999 |
| 0.0015        | 5.0   | 3160 | 0.0137          | 0.7240    | 0.7413 | 0.7325 | 0.9994   | 0.9938  | 0.9998 |


### Framework versions

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