--- license: mit base_model: Amna100/PreTraining-MLM tags: - generated_from_trainer metrics: - precision - recall - f1 model-index: - name: fold_2 results: [] --- [Visualize in Weights & Biases](https://wandb.ai/amnasaeed100/FineTuning-ADE-change1/runs/94wgcdtp) [Visualize in Weights & Biases](https://wandb.ai/amnasaeed100/FineTuning-ADE-change1/runs/8g0cixov) [Visualize in Weights & Biases](https://wandb.ai/amnasaeed100/FineTuning-ADE-change1/runs/05nc4r5u) [Visualize in Weights & Biases](https://wandb.ai/amnasaeed100/FineTuning-ADE-change1/runs/2tfkcyde) [Visualize in Weights & Biases](https://wandb.ai/amnasaeed100/FineTuning-ADE-change1/runs/2zf1k4id) # 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: 0.0123 - Precision: 0.1834 - Recall: 0.3042 - F1: 0.5248 - Pr Auc: 0.6254 - Roc Auc: 0.9352 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Pr Auc | Roc Auc | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:------:|:-------:| | 0.0324 | 1.0 | 630 | 0.0108 | 0.3745 | 0.9507 | 0.7320 | 0.5652 | 0.9573 | | 0.0101 | 2.0 | 1260 | 0.0101 | 0.5987 | 0.1560 | 0.1560 | 0.5987 | 0.9585 | | 0.0067 | 3.0 | 1890 | 0.0114 | 0.0581 | 0.8662 | 0.6011 | 0.6210 | 0.9303 | | 0.0015 | 4.0 | 2520 | 0.0110 | 0.7081 | 0.0206 | 0.9699 | 0.5885 | 0.9400 | | 0.0006 | 5.0 | 3150 | 0.0123 | 0.8324 | 0.2123 | 0.1818 | 0.6254 | 0.9352 | ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1