---
license: mit
base_model: Amna100/PreTraining-MLM
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fold_10
results: []
---
[](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/lvieenf2)
[](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/fgis28rc)
[](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/9tw0vsla)
[](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/ccjl3n87)
[](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/geyuezlx)
[](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/sv9tcfx8)
[](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/9rg5cz4h)
[](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/3fdbnjrq)
[](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/l78entvo)
[](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/s3e8xbt2)
[](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/wgkbnjuf)
# fold_10
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: 0.0103
- Precision: 0.7508
- Recall: 0.5791
- F1: 0.6538
- Accuracy: 0.9992
- Roc Auc: 0.9980
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:------:|
| 0.0258 | 1.0 | 632 | 0.0109 | 0.7529 | 0.4745 | 0.5821 | 0.9992 | 0.9977 | 0.9999 |
| 0.0104 | 2.0 | 1264 | 0.0103 | 0.7508 | 0.5791 | 0.6538 | 0.9992 | 0.9980 | 0.9999 |
| 0.0058 | 3.0 | 1896 | 0.0116 | 0.7394 | 0.6764 | 0.7065 | 0.9993 | 0.9967 | 0.9999 |
| 0.0024 | 4.0 | 2528 | 0.0133 | 0.7740 | 0.6667 | 0.7163 | 0.9993 | 0.9956 | 0.9998 |
| 0.0013 | 5.0 | 3160 | 0.0144 | 0.7581 | 0.6861 | 0.7203 | 0.9993 | 0.9928 | 0.9998 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1