my_awesome_model

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.7362
• Accuracy: {'accuracy': 0.7291666666666666}
• F1: {'f1': 0.7417218543046359}
• Recall: {'recall': 0.7417218543046358}
• Auc: {'roc_auc': 0.7285251607289602}

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: 2e-05
• train_batch_size: 64
• eval_batch_size: 64
• 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	Accuracy	F1	Recall	Auc
No log	0.91	10	0.5662	{'accuracy': 0.6875}	{'f1': 0.7}	{'recall': 0.695364238410596}	{'roc_auc': 0.6870981775994585}
No log	1.82	20	0.5665	{'accuracy': 0.6909722222222222}	{'f1': 0.6832740213523132}	{'recall': 0.6357615894039735}	{'roc_auc': 0.693793203461111}
No log	2.73	30	0.5643	{'accuracy': 0.7256944444444444}	{'f1': 0.7127272727272727}	{'recall': 0.6490066225165563}	{'roc_auc': 0.729612800309373}
No log	3.64	40	0.5743	{'accuracy': 0.7465277777777778}	{'f1': 0.750853242320819}	{'recall': 0.7284768211920529}	{'roc_auc': 0.7474500894281433}
No log	4.55	50	0.6057	{'accuracy': 0.7430555555555556}	{'f1': 0.7448275862068965}	{'recall': 0.7152317880794702}	{'roc_auc': 0.7444772079083483}
No log	5.45	60	0.6318	{'accuracy': 0.7291666666666666}	{'f1': 0.7382550335570469}	{'recall': 0.7284768211920529}	{'roc_auc': 0.7292019142456615}
No log	6.36	70	0.6664	{'accuracy': 0.7291666666666666}	{'f1': 0.7450980392156863}	{'recall': 0.7549668874172185}	{'roc_auc': 0.7278484072122589}
No log	7.27	80	0.7007	{'accuracy': 0.7222222222222222}	{'f1': 0.7241379310344827}	{'recall': 0.695364238410596}	{'roc_auc': 0.7235945279644221}
No log	8.18	90	0.7178	{'accuracy': 0.7326388888888888}	{'f1': 0.7458745874587459}	{'recall': 0.7483443708609272}	{'roc_auc': 0.7318364190071059}
No log	9.09	100	0.7396	{'accuracy': 0.7256944444444444}	{'f1': 0.7285223367697595}	{'recall': 0.7019867549668874}	{'roc_auc': 0.7269057862425677}
No log	10.0	110	0.7362	{'accuracy': 0.7291666666666666}	{'f1': 0.7417218543046359}	{'recall': 0.7417218543046358}	{'roc_auc': 0.7285251607289602}

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.0