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bert-ner-msra – AI Model by PassbyGrocer | AlphaNeural AI

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PassbyGrocer/
bert-ner-msra

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token-classification

generated_from_trainer

google-bert/bert-base-chinese

autotrain_compatible

endpoints_compatible

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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