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---
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
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