distilbert_sa_GLUE_Experiment_logit_kd_mrpc_256 – AI Model by gokuls | AlphaNeural AI

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gokuls/
distilbert_sa_GLUE_Experiment_logit_kd_mrpc_256

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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distilbert_sa_GLUE_Experiment_logit_kd_mrpc_256

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

Loss: 0.5199
Accuracy: 0.3284
F1: 0.0616
Combined Score: 0.1950

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
0.5375	1.0	15	0.5292	0.3162	0.0	0.1581
0.5305	2.0	30	0.5292	0.3162	0.0	0.1581
0.5294	3.0	45	0.5293	0.3162	0.0	0.1581
0.5283	4.0	60	0.5284	0.3162	0.0	0.1581
0.5258	5.0	75	0.5260	0.3162	0.0	0.1581
0.519	6.0	90	0.5199	0.3284	0.0616	0.1950
0.5036	7.0	105	0.5200	0.3848	0.2462	0.3155
0.4916	8.0	120	0.5226	0.4167	0.3239	0.3703
0.4725	9.0	135	0.5298	0.4289	0.3581	0.3935
0.4537	10.0	150	0.5333	0.6152	0.6736	0.6444
0.4382	11.0	165	0.5450	0.6201	0.6906	0.6554

Framework versions

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