distilbert-base-uncased-distilled-clinc

This model is a fine-tuned with knowledge distillation version of distilbert-base-uncased on the clinc_oos dataset. The model is used in Chapter 8: Making Transformers Efficient in Production in the NLP with Transformers book. You can find the full code in the accompanying Github repository.

It achieves the following results on the evaluation set:

• Loss: 0.1005
• Accuracy: 0.9394

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.9031	1.0	318	0.5745	0.7365
0.4481	2.0	636	0.2856	0.8748
0.2528	3.0	954	0.1798	0.9187
0.176	4.0	1272	0.1398	0.9294
0.1416	5.0	1590	0.1211	0.9348
0.1243	6.0	1908	0.1116	0.9348
0.1133	7.0	2226	0.1062	0.9377
0.1075	8.0	2544	0.1035	0.9387
0.1039	9.0	2862	0.1014	0.9381
0.1018	10.0	3180	0.1005	0.9394

Framework versions

• Transformers 4.11.3
• Pytorch 1.9.1+cu102
• Datasets 1.13.0
• Tokenizers 0.10.3