tiny_bert_km_5_v2_qqp – AI Model by Hartunka | AlphaNeural AI

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Hartunka/
tiny_bert_km_5_v2_qqp

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text-classification

generated_from_trainer

Hartunka/tiny_bert_km_5_v2

autotrain_compatible

endpoints_compatible

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tiny_bert_km_5_v2_qqp

This model is a fine-tuned version of Hartunka/tiny_bert_km_5_v2 on the GLUE QQP dataset. It achieves the following results on the evaluation set:

Loss: 0.4044
Accuracy: 0.8236
F1: 0.7516
Combined Score: 0.7876

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
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Combined Score
0.4976	1.0	1422	0.4732	0.7705	0.6348	0.7027
0.4095	2.0	2844	0.4118	0.8045	0.7307	0.7676
0.3541	3.0	4266	0.4059	0.8161	0.7445	0.7803
0.3088	4.0	5688	0.4044	0.8236	0.7516	0.7876
0.2715	5.0	7110	0.4359	0.8229	0.7362	0.7796
0.2395	6.0	8532	0.4361	0.8292	0.7621	0.7956
0.2125	7.0	9954	0.4422	0.8311	0.7741	0.8026
0.1876	8.0	11376	0.4813	0.8353	0.7755	0.8054
0.1677	9.0	12798	0.5044	0.8330	0.7809	0.8070

Framework versions

Transformers 4.50.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.21.1