--- license: mit base_model: Amna100/PreTraining-MLM tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: fold_7 results: [] --- [Visualize in Weights & Biases](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/lvieenf2) [Visualize in Weights & Biases](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/fgis28rc) [Visualize in Weights & Biases](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/9tw0vsla) [Visualize in Weights & Biases](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/ccjl3n87) [Visualize in Weights & Biases](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/geyuezlx) [Visualize in Weights & Biases](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/sv9tcfx8) [Visualize in Weights & Biases](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/9rg5cz4h) [Visualize in Weights & Biases](https://wandb.ai/amnasaeed100/FineTuning-ADE-Repeatedfold/runs/3fdbnjrq) # fold_7 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: 0.0148 - Precision: 0.7317 - Recall: 0.4965 - F1: 0.5915 - Accuracy: 0.9991 - Roc Auc: 0.9926 - 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:-------:|:------:| | 0.0319 | 1.0 | 632 | 0.0148 | 0.7317 | 0.4965 | 0.5915 | 0.9991 | 0.9926 | 0.9998 | | 0.0105 | 2.0 | 1264 | 0.0153 | 0.7528 | 0.6265 | 0.6839 | 0.9992 | 0.9904 | 0.9998 | | 0.0063 | 3.0 | 1896 | 0.0150 | 0.7463 | 0.7092 | 0.7273 | 0.9993 | 0.9794 | 0.9998 | | 0.0025 | 4.0 | 2528 | 0.0174 | 0.7412 | 0.6974 | 0.7186 | 0.9992 | 0.9783 | 0.9997 | ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1