distilbert-base-uncased-distilled-clinc

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

• Loss: 0.5735
• Accuracy: 0.9594
• F1: 0.9588
• Recall: 0.9594

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: 0.0002
• train_batch_size: 256
• eval_batch_size: 256
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 8

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Recall
No log	1.6667	100	1.1619	0.9494	0.9486	0.9494
No log	3.3333	200	0.7192	0.9568	0.9563	0.9568
No log	5.0	300	0.6113	0.9584	0.9577	0.9584
No log	6.6667	400	0.5735	0.9594	0.9588	0.9594

Framework versions

• Transformers 4.56.1
• Pytorch 2.8.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.0