README.md

metadata

license: mit
base_model: Amna100/PreTraining-MLM
tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: fold_1
    results: []

fold_1

This model is a fine-tuned version of Amna100/PreTraining-MLM on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0113
• Precision: 0.7155
• Recall: 0.7233
• F1: 0.7194
• Accuracy: 0.9993
• 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
0.0241	1.0	632	0.0123	0.6317	0.6427	0.6371	0.9991	0.9958	0.9999
0.0098	2.0	1264	0.0114	0.8436	0.6580	0.7393	0.9994	0.9946	0.9998
0.0062	3.0	1896	0.0113	0.7155	0.7233	0.7194	0.9993	0.9952	0.9999
0.0021	4.0	2528	0.0132	0.7990	0.6841	0.7371	0.9993	0.9944	0.9998
0.0013	5.0	3160	0.0145	0.7783	0.7037	0.7391	0.9993	0.9956	0.9998
0.001	6.0	3792	0.0159	0.7730	0.7495	0.7611	0.9994	0.9958	0.9998
0.0004	7.0	4424	0.0143	0.7104	0.7908	0.7485	0.9993	0.9964	0.9999
0.0002	8.0	5056	0.0173	0.8005	0.7255	0.7611	0.9994	0.9951	0.9999
0.0002	9.0	5688	0.0176	0.7981	0.7233	0.7589	0.9993	0.9948	0.9999

Framework versions

• Transformers 4.41.0.dev0
• Pytorch 2.2.1+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1