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---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: ratish/DBERT_CleanDesc_Mode_v10
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ratish/DBERT_CleanDesc_Mode_v10
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0060
- Validation Loss: 0.2047
- Train Accuracy: 0.925
- Epoch: 14
## 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 4620, '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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6863 | 0.6733 | 0.475 | 0 |
| 0.5131 | 0.3023 | 0.95 | 1 |
| 0.2499 | 0.1768 | 0.95 | 2 |
| 0.1463 | 0.1702 | 0.925 | 3 |
| 0.1212 | 0.1646 | 0.9 | 4 |
| 0.0897 | 0.1807 | 0.925 | 5 |
| 0.0689 | 0.1947 | 0.925 | 6 |
| 0.0544 | 0.1885 | 0.925 | 7 |
| 0.0476 | 0.1888 | 0.925 | 8 |
| 0.0325 | 0.2012 | 0.925 | 9 |
| 0.0229 | 0.1717 | 0.925 | 10 |
| 0.0113 | 0.2052 | 0.95 | 11 |
| 0.0087 | 0.1650 | 0.925 | 12 |
| 0.0063 | 0.1987 | 0.95 | 13 |
| 0.0060 | 0.2047 | 0.925 | 14 |
### Framework versions
- Transformers 4.28.1
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
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