distilbert_add_GLUE_Experiment_logit_kd_mnli_256 – AI Model by gokuls | AlphaNeural AI

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gokuls/
distilbert_add_GLUE_Experiment_logit_kd_mnli_256

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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distilbert_add_GLUE_Experiment_logit_kd_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: 0.5429
Accuracy: 0.5632

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
0.6159	1.0	1534	0.5957	0.4585
0.5816	2.0	3068	0.5643	0.5034
0.5632	3.0	4602	0.5612	0.5090
0.5542	4.0	6136	0.5529	0.5238
0.5478	5.0	7670	0.5510	0.5363
0.5424	6.0	9204	0.5511	0.5222
0.5376	7.0	10738	0.5523	0.5268
0.5328	8.0	12272	0.5537	0.5278
0.5267	9.0	13806	0.5482	0.5508
0.5187	10.0	15340	0.5505	0.5422
0.5096	11.0	16874	0.5406	0.5647
0.4998	12.0	18408	0.5438	0.5625
0.4893	13.0	19942	0.5415	0.5765
0.4793	14.0	21476	0.5417	0.5762
0.4698	15.0	23010	0.5425	0.5796
0.4609	16.0	24544	0.5497	0.5824

Framework versions

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