distilbert-ner-query

This model is a fine-tuned version of distilbert/distilbert-base-cased on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.0852
• Token Accuracy: 0.9823
• Token Macro F1: 0.9301
• Entity Precision: 0.8311
• Entity Recall: 0.8578
• Entity F1: 0.8442

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: 32
• eval_batch_size: 32
• 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: 10.0

Training results

Framework versions

• Transformers 4.56.0
• Pytorch 2.14.1+cu130
• Datasets 5.0.1
• Tokenizers 0.22.2