distilbert_sa_GLUE_Experiment_qqp_256 – AI Model by gokuls | AlphaNeural AI

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gokuls/
distilbert_sa_GLUE_Experiment_qqp_256

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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distilbert_sa_GLUE_Experiment_qqp_256

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: 0.4425
Accuracy: 0.8030
F1: 0.7323
Combined Score: 0.7677

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
0.53	1.0	1422	0.5023	0.7557	0.6592	0.7075
0.479	2.0	2844	0.4823	0.7679	0.6483	0.7081
0.4522	3.0	4266	0.4788	0.7741	0.6474	0.7108
0.4263	4.0	5688	0.4753	0.7829	0.6911	0.7370
0.4009	5.0	7110	0.4536	0.7906	0.7194	0.7550
0.3772	6.0	8532	0.4497	0.7949	0.7200	0.7574
0.3548	7.0	9954	0.4453	0.8010	0.7201	0.7606
0.3332	8.0	11376	0.4425	0.8030	0.7323	0.7677
0.3132	9.0	12798	0.4654	0.7938	0.7375	0.7657
0.2951	10.0	14220	0.4551	0.8056	0.7423	0.7739
0.2777	11.0	15642	0.4675	0.8120	0.7374	0.7747
0.2625	12.0	17064	0.4946	0.8082	0.7451	0.7766
0.2473	13.0	18486	0.5041	0.8102	0.7469	0.7786

Framework versions

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