This model is a fine-tuned version of google/bert_uncased_L-4_H-256_A-4 on the GLUE MRPC dataset. It achieves the following results on the evaluation set:

Loss: 0.5071
Accuracy: 0.7721
F1: 0.8394
Combined Score: 0.8057

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: 256
eval_batch_size: 256
seed: 10
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Combined Score
0.6375	1.0	15	0.6024	0.6936	0.8170	0.7553
0.594	2.0	30	0.5776	0.6985	0.8167	0.7576
0.5504	3.0	45	0.5475	0.7279	0.8274	0.7777
0.5155	4.0	60	0.5083	0.7598	0.8345	0.7971
0.4668	5.0	75	0.5116	0.7598	0.8345	0.7971
0.4292	6.0	90	0.5237	0.7696	0.8433	0.8065
0.3859	7.0	105	0.5071	0.7721	0.8394	0.8057
0.3455	8.0	120	0.5300	0.7721	0.8426	0.8073
0.3049	9.0	135	0.5408	0.7721	0.8410	0.8065
0.2735	10.0	150	0.5337	0.7745	0.8425	0.8085
0.2454	11.0	165	0.5962	0.7647	0.84	0.8024
0.2117	12.0	180	0.5756	0.7794	0.8469	0.8132

Framework versions

Transformers 4.46.3
Pytorch 2.2.1+cu118
Datasets 2.17.0
Tokenizers 0.20.3

gokulsrinivasagan/bert_uncased_L-4_H-256_A-4_mrpc

bert_uncased_L-4_H-256_A-4_mrpc

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gokulsrinivasagan/
bert_uncased_L-4_H-256_A-4_mrpc