distilbert_sa_GLUE_Experiment_logit_kd_data_aug_qqp – AI Model by gokuls | AlphaNeural AI

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gokuls/
distilbert_sa_GLUE_Experiment_logit_kd_data_aug_qqp

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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distilbert_sa_GLUE_Experiment_logit_kd_data_aug_qqp

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

Loss: 0.6508
Accuracy: 0.6562
F1: 0.1361
Combined Score: 0.3962

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	F1	Combined Score
0.7744	1.0	29671	0.7100	0.6422	0.0598	0.3510
0.6803	2.0	59342	0.6914	0.6548	0.1291	0.3919
0.6617	3.0	89013	0.6944	0.6588	0.1498	0.4043
0.6531	4.0	118684	0.6699	0.6632	0.1741	0.4187
0.6482	5.0	148355	0.6633	0.6622	0.1666	0.4144
0.6451	6.0	178026	0.6508	0.6562	0.1361	0.3962
0.6431	7.0	207697	0.6632	0.6595	0.1526	0.4060
0.6416	8.0	237368	0.6621	0.6634	0.1720	0.4177
0.6404	9.0	267039	0.6579	0.6691	0.2001	0.4346
0.6395	10.0	296710	0.6554	0.6690	0.2029	0.4359
0.6387	11.0	326381	0.6558	0.6637	0.1755	0.4196

Framework versions

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