distilbert_add_GLUE_Experiment_logit_kd_mnli_384

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

• Loss: 0.5304
• Accuracy: 0.5770

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
0.6035	1.0	1534	0.5764	0.4805
0.5667	2.0	3068	0.5578	0.5171
0.5542	3.0	4602	0.5520	0.5243
0.5447	4.0	6136	0.5460	0.5422
0.5338	5.0	7670	0.5387	0.5671
0.5172	6.0	9204	0.5304	0.5781
0.4993	7.0	10738	0.5333	0.5847
0.482	8.0	12272	0.5317	0.5901
0.4654	9.0	13806	0.5323	0.5949
0.4504	10.0	15340	0.5368	0.5957
0.4369	11.0	16874	0.5405	0.5980

Framework versions

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