cs221-bert-base-uncased-finetuned – AI Model by sercetexam9 | AlphaNeural AI

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sercetexam9/
cs221-bert-base-uncased-finetuned

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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

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: 0.3845
F1: 0.7493
Roc Auc: 0.8124
Accuracy: 0.4224

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: 32
eval_batch_size: 32
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
0.5633	1.0	70	0.5647	0.4607	0.6281	0.1516
0.4272	2.0	140	0.4229	0.6798	0.7598	0.3484
0.3367	3.0	210	0.3808	0.7315	0.7980	0.4061
0.257	4.0	280	0.3716	0.7353	0.8001	0.4188
0.1934	5.0	350	0.3845	0.7493	0.8124	0.4224
0.1511	6.0	420	0.3917	0.7425	0.8063	0.4242
0.1056	7.0	490	0.4091	0.7338	0.7981	0.4368
0.0816	8.0	560	0.4206	0.7463	0.8093	0.4513

Framework versions

Transformers 4.47.1
Pytorch 2.4.0
Datasets 3.0.1
Tokenizers 0.21.0