---
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/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)
# 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: 0.8172
- Recall: 0.5823
- F1: 0.6801
- Accuracy: 0.9993
- Roc Auc: 0.9933
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:------:|
| 0.0326        | 1.0   | 632  | 0.0138          | 0.6507    | 0.4668 | 0.5436 | 0.9991   | 0.9909  | 0.9998 |
| 0.0116        | 2.0   | 1264 | 0.0113          | 0.8172    | 0.5823 | 0.6801 | 0.9993   | 0.9933  | 0.9998 |
| 0.0071        | 3.0   | 1896 | 0.0131          | 0.7896    | 0.5995 | 0.6816 | 0.9993   | 0.9915  | 0.9998 |
| 0.0023        | 4.0   | 2528 | 0.0165          | 0.6275    | 0.6953 | 0.6597 | 0.9991   | 0.9899  | 0.9998 |
| 0.0013        | 5.0   | 3160 | 0.0173          | 0.7465    | 0.6585 | 0.6997 | 0.9993   | 0.9827  | 0.9996 |


### Framework versions

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