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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: ratish/DBERT_CleanDesc_MAKE_v10.1 |
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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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# ratish/DBERT_CleanDesc_MAKE_v10.1 |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-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: 0.1668 |
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- Validation Loss: 0.7903 |
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- Train Accuracy: 0.8 |
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- Epoch: 14 |
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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': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3090, '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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| 2.2016 | 1.9470 | 0.425 | 0 | |
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| 1.6623 | 1.5632 | 0.575 | 1 | |
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| 1.2367 | 1.2743 | 0.575 | 2 | |
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| 0.9547 | 1.1049 | 0.75 | 3 | |
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| 0.7787 | 1.0268 | 0.725 | 4 | |
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| 0.6138 | 0.8950 | 0.75 | 5 | |
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| 0.5122 | 0.9161 | 0.75 | 6 | |
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| 0.4713 | 0.8417 | 0.8 | 7 | |
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| 0.4282 | 0.7698 | 0.75 | 8 | |
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| 0.3625 | 0.7982 | 0.75 | 9 | |
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| 0.2912 | 0.8342 | 0.775 | 10 | |
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| 0.2440 | 0.7864 | 0.775 | 11 | |
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| 0.2136 | 0.7688 | 0.775 | 12 | |
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| 0.1914 | 0.7626 | 0.8 | 13 | |
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| 0.1668 | 0.7903 | 0.8 | 14 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- TensorFlow 2.12.0 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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