CN_BERT_Sci – AI Model by vishwa27 | AlphaNeural AI

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vishwa27/
CN_BERT_Sci

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text-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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CN_BERT_Sci

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: 0.0088
F1: {'f1': 0.9980007996801279}
Accuracy: {'accuracy': 0.998}

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: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Accuracy
0.3819	0.09	1000	0.3378	{'f1': 0.7968977217644208}	{'accuracy': 0.7905}
0.2709	0.18	2000	0.2719	{'f1': 0.92162615255658}	{'accuracy': 0.9252}
0.169	0.27	3000	0.0888	{'f1': 0.9760964045831687}	{'accuracy': 0.9758}
0.0963	0.36	4000	0.0350	{'f1': 0.991297389216765}	{'accuracy': 0.9913}
0.0499	0.44	5000	0.0260	{'f1': 0.9937381969983102}	{'accuracy': 0.9937}
0.0344	0.53	6000	0.0170	{'f1': 0.9963048037551183}	{'accuracy': 0.9963}
0.0307	0.62	7000	0.0213	{'f1': 0.9957991598319663}	{'accuracy': 0.9958}
0.036	0.71	8000	0.0105	{'f1': 0.997700689793062}	{'accuracy': 0.9977}
0.0209	0.8	9000	0.0106	{'f1': 0.9981032245183188}	{'accuracy': 0.9981}
0.0253	0.89	10000	0.0089	{'f1': 0.9981024667931688}	{'accuracy': 0.9981}
0.0231	0.98	11000	0.0088	{'f1': 0.9980007996801279}	{'accuracy': 0.998}

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0