bert-base-uncased_title_fine_tuned

This model is a fine-tuned version of bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.3368
• Accuracy: {'accuracy': 0.8810840405146455}
• Recall: {'recall': 0.8611674554879423}
• Precision: {'precision': 0.890468422279189}
• F1: {'f1': 0.8755728689275893}

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: 24
• eval_batch_size: 24
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 1000
• num_epochs: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall	Precision	F1
0.3224	1.0	3045	0.3079	{'accuracy': 0.8730358609362168}	{'recall': 0.8139508677034032}	{'precision': 0.915346597389431}	{'f1': 0.861676110945422}
0.2818	2.0	6090	0.3153	{'accuracy': 0.8814672871612373}	{'recall': 0.8299526707234618}	{'precision': 0.9182146864480738}	{'f1': 0.8718555785735426}
0.2394	3.0	9135	0.3104	{'accuracy': 0.8830002737476047}	{'recall': 0.8548568852828488}	{'precision': 0.8993479549496147}	{'f1': 0.8765382171124848}
0.204	4.0	12180	0.3368	{'accuracy': 0.8810840405146455}	{'recall': 0.8611674554879423}	{'precision': 0.890468422279189}	{'f1': 0.8755728689275893}

Framework versions

• Transformers 4.21.1
• Pytorch 1.12.0+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1