distilbert-base-uncased_fine_tuned_title

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

• Loss: 1.2615
• Accuracy: {'accuracy': 0.877634820695319}
• Recall: {'recall': 0.8474786132372805}
• Precision: {'precision': 0.8953502200023784}
• F1: {'f1': 0.8707569536806801}

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: 32
• eval_batch_size: 32
• 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: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall	Precision	F1
0.3093	1.0	2284	0.3021	{'accuracy': 0.8779085683000274}	{'recall': 0.8560333183250788}	{'precision': 0.8888499298737728}	{'f1': 0.8721330275229358}
0.2459	2.0	4568	0.2909	{'accuracy': 0.8894059676977827}	{'recall': 0.8513057181449797}	{'precision': 0.9153957879448076}	{'f1': 0.8821882654846612}
0.1696	3.0	6852	0.3259	{'accuracy': 0.8808102929099371}	{'recall': 0.8595227375056281}	{'precision': 0.8915353181552831}	{'f1': 0.875236403232277}
0.1179	4.0	9136	0.4946	{'accuracy': 0.8729811114152751}	{'recall': 0.8610986042323278}	{'precision': 0.8756868131868132}	{'f1': 0.8683314415437005}
0.0775	5.0	11420	0.6547	{'accuracy': 0.8708458800985491}	{'recall': 0.8041422782530392}	{'precision': 0.9202627850057967}	{'f1': 0.8582927854868745}
0.0522	6.0	13704	0.6699	{'accuracy': 0.8768683274021353}	{'recall': 0.8325078793336335}	{'precision': 0.9067058967757754}	{'f1': 0.8680241769849187}
0.0406	7.0	15988	0.8149	{'accuracy': 0.8739118532712838}	{'recall': 0.8330706888788834}	{'precision': 0.9002554433767181}	{'f1': 0.8653610055539316}
0.0298	8.0	18272	0.8906	{'accuracy': 0.8753353408157679}	{'recall': 0.8421882035119316}	{'precision': 0.8952973555103506}	{'f1': 0.8679310944840787}
0.0217	9.0	20556	1.0192	{'accuracy': 0.8754448398576512}	{'recall': 0.8624493471409275}	{'precision': 0.8791738382099827}	{'f1': 0.8707312915506562}
0.017	10.0	22840	1.0550	{'accuracy': 0.8758828360251848}	{'recall': 0.8556956325979289}	{'precision': 0.8852917200419238}	{'f1': 0.8702421155056951}
0.0139	11.0	25124	1.0873	{'accuracy': 0.8728716123733917}	{'recall': 0.8582845565060784}	{'precision': 0.8776473296500921}	{'f1': 0.8678579558388345}
0.0114	12.0	27408	1.1506	{'accuracy': 0.8716123733917328}	{'recall': 0.8628995947771274}	{'precision': 0.8718298646650745}	{'f1': 0.8673417435085139}
0.0061	13.0	29692	1.2574	{'accuracy': 0.8696961401587736}	{'recall': 0.874943719045475}	{'precision': 0.8596549435965495}	{'f1': 0.8672319535869686}
0.0035	14.0	31976	1.2490	{'accuracy': 0.8784560635094443}	{'recall': 0.85006753714543}	{'precision': 0.8947867298578199}	{'f1': 0.8718540752713001}
0.0028	15.0	34260	1.2615	{'accuracy': 0.877634820695319}	{'recall': 0.8474786132372805}	{'precision': 0.8953502200023784}	{'f1': 0.8707569536806801}

Framework versions

• Transformers 4.21.0
• Pytorch 1.12.0+cu113
• Datasets 2.4.0
• Tokenizers 0.12.1