This model is a fine-tuned version of gokulsrinivasagan/bert_base_lda_5 on the GLUE MRPC dataset. It achieves the following results on the evaluation set:

Loss: 0.6226
Accuracy: 0.6838
F1: 0.8122
Combined Score: 0.7480

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: 0.001
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: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Combined Score
1.6067	1.0	15	0.6951	0.6838	0.8122	0.7480
0.6503	2.0	30	0.6270	0.6838	0.8122	0.7480
0.6289	3.0	45	0.6279	0.6838	0.8122	0.7480
0.6364	4.0	60	0.6244	0.6838	0.8122	0.7480
0.6368	5.0	75	0.6272	0.6838	0.8122	0.7480
0.6349	6.0	90	0.6252	0.6838	0.8122	0.7480
0.6311	7.0	105	0.6263	0.6838	0.8122	0.7480
0.6339	8.0	120	0.6240	0.6838	0.8122	0.7480
0.6296	9.0	135	0.6287	0.6838	0.8122	0.7480
0.6346	10.0	150	0.6260	0.6838	0.8122	0.7480
0.6359	11.0	165	0.6275	0.6838	0.8122	0.7480
0.6337	12.0	180	0.6242	0.6838	0.8122	0.7480
0.6302	13.0	195	0.6228	0.6838	0.8122	0.7480
0.6351	14.0	210	0.6258	0.6838	0.8122	0.7480
0.6324	15.0	225	0.6226	0.6838	0.8122	0.7480
0.6301	16.0	240	0.6243	0.6838	0.8122	0.7480
0.6316	17.0	255	0.6248	0.6838	0.8122	0.7480
0.63	18.0	270	0.6243	0.6838	0.8122	0.7480
0.6304	19.0	285	0.6230	0.6838	0.8122	0.7480
0.633	20.0	300	0.6250	0.6838	0.8122	0.7480

Framework versions

Transformers 4.46.3
Pytorch 2.2.1+cu118
Datasets 2.17.0
Tokenizers 0.20.3

gokulsrinivasagan/bert_base_lda_5_mrpc

bert_base_lda_5_mrpc

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gokulsrinivasagan/
bert_base_lda_5_mrpc