fold_11 / README.md
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---
license: mit
base_model: Amna100/PreTraining-MLM
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: fold_11
results: []
---
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[<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)
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# fold_11
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: 0.0112
- Precision: 0.7083
- Recall: 0.6886
- F1: 0.6983
- Accuracy: 0.9993
- Roc Auc: 0.9950
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:------:|
| 0.0313 | 1.0 | 632 | 0.0125 | 0.7457 | 0.5494 | 0.6327 | 0.9992 | 0.9881 | 0.9997 |
| 0.0127 | 2.0 | 1264 | 0.0117 | 0.7078 | 0.6684 | 0.6875 | 0.9993 | 0.9949 | 0.9998 |
| 0.0061 | 3.0 | 1896 | 0.0112 | 0.7083 | 0.6886 | 0.6983 | 0.9993 | 0.9950 | 0.9999 |
| 0.0024 | 4.0 | 2528 | 0.0159 | 0.8163 | 0.6076 | 0.6967 | 0.9994 | 0.9893 | 0.9997 |
| 0.0016 | 5.0 | 3160 | 0.0153 | 0.7652 | 0.6684 | 0.7135 | 0.9993 | 0.9931 | 0.9998 |
| 0.0008 | 6.0 | 3792 | 0.0170 | 0.7798 | 0.6633 | 0.7168 | 0.9994 | 0.9921 | 0.9998 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1