bert-ner-msra

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

• eval_loss: 0.0413
• eval_precision: 0.9481
• eval_recall: 0.9507
• eval_f1: 0.9494
• eval_accuracy: 0.9939
• eval_runtime: 10.3612
• eval_samples_per_second: 421.283
• eval_steps_per_second: 13.222
• epoch: 9.0
• step: 13041

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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 10
• mixed_precision_training: Native AMP

Framework versions

• Transformers 4.46.1
• Pytorch 2.4.1+cu124
• Datasets 3.1.0
• Tokenizers 0.20.1