distilbert_sa_GLUE_Experiment_qqp_192 – AI Model by gokuls | AlphaNeural AI

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gokuls/
distilbert_sa_GLUE_Experiment_qqp_192

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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distilbert_sa_GLUE_Experiment_qqp_192

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: 0.4568
Accuracy: 0.7910
F1: 0.7234
Combined Score: 0.7572

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
0.5339	1.0	1422	0.5031	0.7551	0.6484	0.7018
0.4835	2.0	2844	0.4866	0.7650	0.6504	0.7077
0.4587	3.0	4266	0.4792	0.7694	0.6422	0.7058
0.4369	4.0	5688	0.4851	0.7745	0.6716	0.7230
0.4155	5.0	7110	0.4705	0.7791	0.6970	0.7380
0.3961	6.0	8532	0.4633	0.7858	0.7093	0.7476
0.3772	7.0	9954	0.4572	0.7908	0.7176	0.7542
0.3593	8.0	11376	0.4568	0.7910	0.7234	0.7572
0.3422	9.0	12798	0.4661	0.7927	0.7227	0.7577
0.3265	10.0	14220	0.4596	0.7983	0.7290	0.7636
0.3119	11.0	15642	0.4635	0.7977	0.7255	0.7616
0.2961	12.0	17064	0.4857	0.8008	0.7309	0.7659
0.2831	13.0	18486	0.4987	0.8037	0.7314	0.7676

Framework versions

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