distilbert_add_GLUE_Experiment_logit_kd_pretrain_mnli

This model is a fine-tuned version of gokuls/distilbert_add_pre-training-complete on the GLUE MNLI dataset. It achieves the following results on the evaluation set:

• Loss: 0.4768
• Accuracy: 0.6843

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
• distributed_type: multi-GPU
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5762	1.0	1534	0.5291	0.5904
0.5131	2.0	3068	0.4986	0.6470
0.4806	3.0	4602	0.4832	0.6713
0.4563	4.0	6136	0.4803	0.6770
0.4352	5.0	7670	0.4790	0.6832
0.4157	6.0	9204	0.4866	0.6809
0.3984	7.0	10738	0.4938	0.6836
0.3835	8.0	12272	0.4940	0.6841
0.3703	9.0	13806	0.4972	0.6823
0.3592	10.0	15340	0.4992	0.6854

Framework versions

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