bert-to-distilbert-NER

This model is a fine-tuned version of dslim/bert-base-NER on the conll2003 dataset. It achieves the following results on the evaluation set:

• eval_loss: 5.9063
• eval_precision: 0.0120
• eval_recall: 0.0069
• eval_f1: 0.0088
• eval_accuracy: 0.7600
• eval_runtime: 8.6309
• eval_samples_per_second: 376.671
• eval_steps_per_second: 3.012
• epoch: 1.0
• step: 110

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: 0.00023888106906613202
• train_batch_size: 128
• eval_batch_size: 128
• seed: 33
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2
• mixed_precision_training: Native AMP

Framework versions

• Transformers 4.19.1
• Pytorch 1.11.0+cu113
• Datasets 2.2.1
• Tokenizers 0.12.1