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--- |
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base_model: bert-base-chinese |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: AIYIYA/my_aa |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information Keras had access to. You should |
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probably proofread and complete it, then remove this comment. --> |
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# AIYIYA/my_aa |
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This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.7596 |
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- Validation Loss: 1.4913 |
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- Train Accuracy: 0.6753 |
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- Epoch: 29 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- 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} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Validation Loss | Train Accuracy | Epoch | |
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|:----------:|:---------------:|:--------------:|:-----:| |
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| 3.4316 | 3.2876 | 0.2078 | 0 | |
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| 3.0452 | 3.0083 | 0.2338 | 1 | |
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| 2.6954 | 2.7106 | 0.3766 | 2 | |
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| 2.3815 | 2.4910 | 0.4935 | 3 | |
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| 2.0499 | 2.3035 | 0.5584 | 4 | |
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| 1.8322 | 2.1419 | 0.5844 | 5 | |
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| 1.6292 | 1.9997 | 0.6104 | 6 | |
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| 1.4675 | 1.8933 | 0.6234 | 7 | |
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| 1.3115 | 1.8016 | 0.5974 | 8 | |
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| 1.2088 | 1.7273 | 0.6364 | 9 | |
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| 1.1053 | 1.6728 | 0.6623 | 10 | |
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| 1.0254 | 1.6284 | 0.6364 | 11 | |
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| 0.9600 | 1.6252 | 0.6494 | 12 | |
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| 0.9058 | 1.5662 | 0.6623 | 13 | |
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| 0.8675 | 1.5423 | 0.6623 | 14 | |
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| 0.8434 | 1.5208 | 0.6753 | 15 | |
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| 0.8356 | 1.5140 | 0.6753 | 16 | |
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| 0.8070 | 1.5024 | 0.6753 | 17 | |
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| 0.7749 | 1.4941 | 0.6753 | 18 | |
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| 0.7805 | 1.4913 | 0.6753 | 19 | |
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| 0.7764 | 1.4913 | 0.6753 | 20 | |
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| 0.7630 | 1.4913 | 0.6753 | 21 | |
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| 0.7806 | 1.4913 | 0.6753 | 22 | |
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| 0.7665 | 1.4913 | 0.6753 | 23 | |
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| 0.7803 | 1.4913 | 0.6753 | 24 | |
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| 0.7778 | 1.4913 | 0.6753 | 25 | |
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| 0.7781 | 1.4913 | 0.6753 | 26 | |
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| 0.7798 | 1.4913 | 0.6753 | 27 | |
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| 0.7845 | 1.4913 | 0.6753 | 28 | |
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| 0.7596 | 1.4913 | 0.6753 | 29 | |
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### Framework versions |
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- Transformers 4.31.0 |
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- TensorFlow 2.12.0 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
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