distilbert_add_GLUE_Experiment_mnli_256

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: 0.9622
• Accuracy: 0.5456

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
1.0905	1.0	1534	1.0415	0.4633
1.0344	2.0	3068	1.0571	0.4569
1.0137	3.0	4602	1.0080	0.4888
0.9902	4.0	6136	0.9876	0.5090
0.9733	5.0	7670	0.9744	0.5134
0.9596	6.0	9204	0.9726	0.5138
0.9484	7.0	10738	0.9684	0.5233
0.938	8.0	12272	0.9715	0.5150
0.9279	9.0	13806	0.9707	0.5237
0.9188	10.0	15340	0.9580	0.5319
0.9102	11.0	16874	0.9561	0.5396
0.9011	12.0	18408	0.9594	0.5368
0.893	13.0	19942	0.9641	0.5345
0.8845	14.0	21476	0.9671	0.5367
0.8751	15.0	23010	0.9553	0.5388
0.8662	16.0	24544	0.9601	0.5447
0.8573	17.0	26078	0.9519	0.5473
0.849	18.0	27612	0.9814	0.5410
0.8404	19.0	29146	0.9733	0.5464
0.832	20.0	30680	0.9671	0.5492
0.8233	21.0	32214	0.9864	0.5509
0.8144	22.0	33748	0.9925	0.5536

Framework versions

• Transformers 4.26.0
• Pytorch 1.14.0a0+410ce96
• Datasets 2.8.0
• Tokenizers 0.13.2