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

Loss: 0.5540
Pearson: 0.8698
Spearmanr: 0.8672
Combined Score: 0.8685

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	Pearson	Spearmanr	Combined Score
3.169	1.0	23	1.2152	0.7365	0.7145	0.7255
0.8972	2.0	46	0.6539	0.8441	0.8419	0.8430
0.6068	3.0	69	0.6525	0.8573	0.8560	0.8566
0.4385	4.0	92	0.5963	0.8648	0.8635	0.8641
0.3591	5.0	115	0.5540	0.8698	0.8672	0.8685
0.2949	6.0	138	0.6457	0.8616	0.8604	0.8610
0.2607	7.0	161	0.5848	0.8650	0.8620	0.8635
0.1898	8.0	184	0.6165	0.8638	0.8623	0.8630
0.1669	9.0	207	0.5749	0.8640	0.8598	0.8619
0.1298	10.0	230	0.5566	0.8676	0.8642	0.8659

Framework versions

Transformers 4.46.3
Pytorch 2.2.1+cu118
Datasets 2.17.0
Tokenizers 0.20.3

gokulsrinivasagan/distilbert-base-uncased_stsb

distilbert-base-uncased_stsb

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

gokulsrinivasagan/
distilbert-base-uncased_stsb