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---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: LovenOO/distilBERT_without_preprocessing_grid_search
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. -->
# LovenOO/distilBERT_without_preprocessing_grid_search
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: 0.1059
- Validation Loss: 0.5285
- Train Precision: 0.7113
- Train Recall: 0.6835
- Train F1: 0.6900
- Train Accuracy: 0.8635
- Epoch: 7
## 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', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 5140, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 1.1440 | 0.6108 | 0.5739 | 0.5788 | 0.5727 | 0.8367 | 0 |
| 0.5050 | 0.5447 | 0.6506 | 0.6243 | 0.6325 | 0.8489 | 1 |
| 0.3584 | 0.4823 | 0.6518 | 0.6619 | 0.6558 | 0.8620 | 2 |
| 0.2612 | 0.4890 | 0.7183 | 0.6777 | 0.6902 | 0.8654 | 3 |
| 0.1901 | 0.4922 | 0.7137 | 0.6880 | 0.6937 | 0.8639 | 4 |
| 0.1566 | 0.5050 | 0.7220 | 0.6838 | 0.6953 | 0.8703 | 5 |
| 0.1189 | 0.5284 | 0.7088 | 0.6911 | 0.6920 | 0.8712 | 6 |
| 0.1059 | 0.5285 | 0.7113 | 0.6835 | 0.6900 | 0.8635 | 7 |
### Framework versions
- Transformers 4.24.0
- TensorFlow 2.13.0
- Datasets 2.14.2
- Tokenizers 0.11.0
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