distilbert_sa_GLUE_Experiment_qnli_256 – AI Model by gokuls | AlphaNeural AI

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distilbert_sa_GLUE_Experiment_qnli_256

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text-classification

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autotrain_compatible

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distilbert_sa_GLUE_Experiment_qnli_256

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

Loss: 0.6564
Accuracy: 0.6030

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
0.679	1.0	410	0.6614	0.5938
0.6496	2.0	820	0.6564	0.6030
0.6268	3.0	1230	0.6635	0.5978
0.6055	4.0	1640	0.6714	0.5933
0.5836	5.0	2050	0.6964	0.5913
0.5602	6.0	2460	0.7319	0.5832
0.5385	7.0	2870	0.7653	0.5718

Framework versions

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