---
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-change2/runs/zkyqf4w8)
[<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-change2/runs/n6lnsbeg)
[<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-change2/runs/k9jhon43)
[<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-change2/runs/67sviuwh)
# 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: 0.0104
- Precision: 0.6792
- Recall: 0.5870
- F1: 0.6297
- Accuracy: 0.9993
- Roc Auc: 0.9967
- 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy | Roc Auc | Pr Auc |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:------:|
| 0.0252        | 1.0   | 711  | 0.0159          | 0.4538    | 0.6413 | 0.5315 | 0.9988   | 0.9944  | 0.9998 |
| 0.0095        | 2.0   | 1422 | 0.0104          | 0.6792    | 0.5870 | 0.6297 | 0.9993   | 0.9967  | 0.9999 |
| 0.003         | 3.0   | 2133 | 0.0106          | 0.6432    | 0.6957 | 0.6684 | 0.9993   | 0.9973  | 0.9999 |
| 0.0024        | 4.0   | 2844 | 0.0126          | 0.7006    | 0.6739 | 0.6870 | 0.9994   | 0.9960  | 0.9999 |
| 0.0004        | 5.0   | 3555 | 0.0148          | 0.7239    | 0.6413 | 0.6801 | 0.9994   | 0.9954  | 0.9999 |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1