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Rami/multi-label-class-classification-on-github-issues

sourceHugging Faceupdated 4y agoView on Hugging Face

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multi-label-class-classification-on-github-issues

This model is a fine-tuned version of neuralmagic/oBERT-12-upstream-pruned-unstructured-97 on the None dataset. It achieves the following results on the evaluation set:

—Loss: 0.1077
—Micro f1: 0.6520
—Macro f1: 0.0704

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: 3e-05
—trainbatchsize: 64
—evalbatchsize: 8
—seed: 42
—optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
—lrschedulertype: linear
—num_epochs: 30
—mixedprecisiontraining: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Micro f1	Macro f1
No log	1.0	49	0.2835	0.3791	0.0172
No log	2.0	98	0.1710	0.3791	0.0172
No log	3.0	147	0.1433	0.3791	0.0172
No log	4.0	196	0.1333	0.4540	0.0291
No log	5.0	245	0.1247	0.5206	0.0352
No log	6.0	294	0.1173	0.6003	0.0541
No log	7.0	343	0.1125	0.6315	0.0671
No log	8.0	392	0.1095	0.6439	0.0699
No log	9.0	441	0.1072	0.6531	0.0713
No log	10.0	490	0.1075	0.6397	0.0695
0.1605	11.0	539	0.1074	0.6591	0.0711
0.1605	12.0	588	0.1043	0.6462	0.0703
0.1605	13.0	637	0.1049	0.6541	0.0709
0.1605	14.0	686	0.1051	0.6524	0.0713
0.1605	15.0	735	0.1061	0.6535	0.0770
0.1605	16.0	784	0.1034	0.6511	0.0708

Framework versions

—Transformers 4.25.1
—Pytorch 1.13.0+cu116
—Datasets 2.8.0
—Tokenizers 0.13.2