my_aa / README.md
AIYIYA's picture
Training in progress epoch 29
a73ff8a
metadata
base_model: bert-base-chinese
tags:
  - generated_from_keras_callback
model-index:
  - name: AIYIYA/my_aa
    results: []

AIYIYA/my_aa

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

  • Train Loss: 0.7596
  • Validation Loss: 1.4913
  • Train Accuracy: 0.6753
  • Epoch: 29

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:

  • optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 280, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
  • training_precision: float32

Training results

Train Loss Validation Loss Train Accuracy Epoch
3.4316 3.2876 0.2078 0
3.0452 3.0083 0.2338 1
2.6954 2.7106 0.3766 2
2.3815 2.4910 0.4935 3
2.0499 2.3035 0.5584 4
1.8322 2.1419 0.5844 5
1.6292 1.9997 0.6104 6
1.4675 1.8933 0.6234 7
1.3115 1.8016 0.5974 8
1.2088 1.7273 0.6364 9
1.1053 1.6728 0.6623 10
1.0254 1.6284 0.6364 11
0.9600 1.6252 0.6494 12
0.9058 1.5662 0.6623 13
0.8675 1.5423 0.6623 14
0.8434 1.5208 0.6753 15
0.8356 1.5140 0.6753 16
0.8070 1.5024 0.6753 17
0.7749 1.4941 0.6753 18
0.7805 1.4913 0.6753 19
0.7764 1.4913 0.6753 20
0.7630 1.4913 0.6753 21
0.7806 1.4913 0.6753 22
0.7665 1.4913 0.6753 23
0.7803 1.4913 0.6753 24
0.7778 1.4913 0.6753 25
0.7781 1.4913 0.6753 26
0.7798 1.4913 0.6753 27
0.7845 1.4913 0.6753 28
0.7596 1.4913 0.6753 29

Framework versions

  • Transformers 4.31.0
  • TensorFlow 2.12.0
  • Datasets 2.13.1
  • Tokenizers 0.13.3