CS221-bert-base-uncased-finetuned-semeval-NT-som – AI Model by Kuongan | AlphaNeural AI

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Kuongan/
CS221-bert-base-uncased-finetuned-semeval-NT-som

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text-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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CS221-bert-base-uncased-finetuned-semeval-NT-som

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

Loss: 0.2468
F1: 0.5157
Roc Auc: 0.7038
Accuracy: 0.6003

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: 8
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	F1	Roc Auc	Accuracy
0.3098	1.0	340	0.2643	0.3195	0.5979	0.5634
0.2215	2.0	680	0.2357	0.4847	0.6787	0.6032
0.1457	3.0	1020	0.2468	0.5157	0.7038	0.6003
0.0879	4.0	1360	0.2827	0.4852	0.6824	0.6018
0.0285	5.0	1700	0.3326	0.4681	0.6919	0.5457
0.0292	6.0	2040	0.3682	0.4992	0.7087	0.5664

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0