distilbert-base-uncased-distilled-clinc – AI Model by jdang | AlphaNeural AI

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jdang/
distilbert-base-uncased-distilled-clinc

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text-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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distilbert-base-uncased-distilled-clinc

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

Loss: 0.0562
Accuracy: 0.9352

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: 2e-05
train_batch_size: 48
eval_batch_size: 48
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 9

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5802	1.0	318	0.3269	0.6658
0.264	2.0	636	0.1590	0.8616
0.1571	3.0	954	0.1035	0.9113
0.1155	4.0	1272	0.0799	0.9223
0.0947	5.0	1590	0.0686	0.9268
0.0839	6.0	1908	0.0624	0.9310
0.0772	7.0	2226	0.0589	0.9323
0.0733	8.0	2544	0.0569	0.9355
0.0713	9.0	2862	0.0562	0.9352

Framework versions

Transformers 4.13.0
Pytorch 1.12.0
Datasets 1.16.1
Tokenizers 0.10.3