---
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/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)
[<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/vp363qmp)
[<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/10xzvwgi)
# 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: 0.0125
- Precision: 0.6887
- Recall: 0.6744
- F1: 0.6814
- Accuracy: 0.9992
- Roc Auc: 0.9939
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:------:|
| 0.0286        | 1.0   | 632  | 0.0159          | 0.5908    | 0.5035 | 0.5436 | 0.9988   | 0.9931  | 0.9998 |
| 0.0115        | 2.0   | 1264 | 0.0125          | 0.6887    | 0.6744 | 0.6814 | 0.9992   | 0.9939  | 0.9998 |
| 0.0079        | 3.0   | 1896 | 0.0170          | 0.8419    | 0.5289 | 0.6496 | 0.9992   | 0.9859  | 0.9995 |
| 0.0035        | 4.0   | 2528 | 0.0150          | 0.7146    | 0.7344 | 0.7244 | 0.9992   | 0.9903  | 0.9997 |
| 0.002         | 5.0   | 3160 | 0.0166          | 0.6471    | 0.7621 | 0.6999 | 0.9991   | 0.9917  | 0.9997 |
| 0.0017        | 6.0   | 3792 | 0.0196          | 0.8300    | 0.6651 | 0.7385 | 0.9993   | 0.9865  | 0.9995 |
| 0.0012        | 7.0   | 4424 | 0.0175          | 0.7143    | 0.7621 | 0.7374 | 0.9993   | 0.9920  | 0.9997 |
| 0.0004        | 8.0   | 5056 | 0.0176          | 0.7262    | 0.7413 | 0.7337 | 0.9992   | 0.9920  | 0.9997 |


### Framework versions

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