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--- |
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license: mit |
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base_model: indolem/indobert-base-uncased |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: wdevinsp/indobert-base-uncased-finetuned-digestive-qna |
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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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# wdevinsp/indobert-base-uncased-finetuned-digestive-qna |
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This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 0.0391 |
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- Train End Logits Accuracy: 0.9814 |
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- Train Start Logits Accuracy: 0.9814 |
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- Epoch: 37 |
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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': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3700, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 | Train End Logits Accuracy | Train Start Logits Accuracy | Epoch | |
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|:----------:|:-------------------------:|:---------------------------:|:-----:| |
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| 3.1872 | 0.3564 | 0.3733 | 0 | |
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| 2.1076 | 0.4189 | 0.4088 | 1 | |
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| 1.6782 | 0.4443 | 0.5507 | 2 | |
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| 1.1711 | 0.5507 | 0.7111 | 3 | |
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| 0.6231 | 0.7720 | 0.8429 | 4 | |
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| 0.4296 | 0.8463 | 0.8902 | 5 | |
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| 0.2544 | 0.9189 | 0.9409 | 6 | |
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| 0.1853 | 0.9358 | 0.9510 | 7 | |
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| 0.1411 | 0.9544 | 0.9578 | 8 | |
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| 0.1215 | 0.9493 | 0.9662 | 9 | |
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| 0.1141 | 0.9645 | 0.9628 | 10 | |
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| 0.1195 | 0.9561 | 0.9696 | 11 | |
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| 0.0851 | 0.9662 | 0.9645 | 12 | |
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| 0.1255 | 0.9527 | 0.9493 | 13 | |
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| 0.0897 | 0.9595 | 0.9730 | 14 | |
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| 0.0860 | 0.9578 | 0.9696 | 15 | |
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| 0.0710 | 0.9595 | 0.9713 | 16 | |
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| 0.0669 | 0.9628 | 0.9713 | 17 | |
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| 0.0634 | 0.9797 | 0.9730 | 18 | |
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| 0.0765 | 0.9662 | 0.9814 | 19 | |
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| 0.0732 | 0.9679 | 0.9730 | 20 | |
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| 0.0586 | 0.9696 | 0.9713 | 21 | |
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| 0.0572 | 0.9679 | 0.9764 | 22 | |
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| 0.0518 | 0.9747 | 0.9764 | 23 | |
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| 0.0500 | 0.9713 | 0.9764 | 24 | |
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| 0.0464 | 0.9696 | 0.9713 | 25 | |
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| 0.0470 | 0.9814 | 0.9730 | 26 | |
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| 0.0575 | 0.9797 | 0.9764 | 27 | |
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| 0.0660 | 0.9679 | 0.9696 | 28 | |
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| 0.0576 | 0.9713 | 0.9696 | 29 | |
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| 0.0515 | 0.9696 | 0.9747 | 30 | |
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| 0.0547 | 0.9780 | 0.9696 | 31 | |
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| 0.0430 | 0.9730 | 0.9780 | 32 | |
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| 0.0408 | 0.9814 | 0.9747 | 33 | |
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| 0.0475 | 0.9747 | 0.9747 | 34 | |
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| 0.0536 | 0.9764 | 0.9797 | 35 | |
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| 0.0494 | 0.9696 | 0.9730 | 36 | |
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| 0.0391 | 0.9814 | 0.9814 | 37 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- TensorFlow 2.15.0 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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