bert-base-uncased-stsb – AI Model by gokuls | AlphaNeural AI

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gokuls/
bert-base-uncased-stsb

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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bert-base-uncased-stsb

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

Loss: 0.4676
Pearson: 0.8901
Spearmanr: 0.8872
Combined Score: 0.8887

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: 128
eval_batch_size: 128
seed: 10
distributed_type: multi-GPU
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Pearson	Spearmanr	Combined Score
2.3939	1.0	45	0.7358	0.8686	0.8653	0.8669
0.5084	2.0	90	0.4959	0.8835	0.8799	0.8817
0.3332	3.0	135	0.5002	0.8846	0.8815	0.8830
0.2202	4.0	180	0.4962	0.8854	0.8827	0.8840
0.1642	5.0	225	0.4848	0.8864	0.8839	0.8852
0.1312	6.0	270	0.4987	0.8872	0.8866	0.8869
0.1057	7.0	315	0.4840	0.8895	0.8848	0.8871
0.0935	8.0	360	0.4753	0.8887	0.8840	0.8863
0.0835	9.0	405	0.4676	0.8901	0.8872	0.8887
0.0749	10.0	450	0.4808	0.8901	0.8867	0.8884
0.0625	11.0	495	0.4760	0.8893	0.8857	0.8875
0.0607	12.0	540	0.5113	0.8899	0.8859	0.8879
0.0564	13.0	585	0.4918	0.8900	0.8860	0.8880
0.0495	14.0	630	0.4749	0.8905	0.8868	0.8887
0.0446	15.0	675	0.4889	0.8888	0.8856	0.8872
0.045	16.0	720	0.4680	0.8918	0.8889	0.8904

Framework versions

Transformers 4.26.0
Pytorch 1.14.0a0+410ce96
Datasets 2.9.0
Tokenizers 0.13.2