| --- |
| license: apache-2.0 |
| base_model: distilbert-base-uncased |
| tags: |
| - generated_from_keras_callback |
| model-index: |
| - name: rsaketh02/sak |
| 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. --> |
|
|
| # rsaketh02/sak |
|
|
| 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: 0.0263 |
| - Validation Loss: 0.0494 |
| - Train Precision: 0.9302 |
| - Train Recall: 0.9394 |
| - Train F1: 0.9348 |
| - Train Accuracy: 0.9873 |
| - Epoch: 2 |
|
|
| ## 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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, '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, 'weight_decay_rate': 0.01} |
| - training_precision: float32 |
| |
| ### Training results |
| |
| | Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch | |
| |:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:| |
| | 0.1686 | 0.0562 | 0.9098 | 0.9256 | 0.9177 | 0.9842 | 0 | |
| | 0.0418 | 0.0494 | 0.9270 | 0.9362 | 0.9316 | 0.9866 | 1 | |
| | 0.0263 | 0.0494 | 0.9302 | 0.9394 | 0.9348 | 0.9873 | 2 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.35.2 |
| - TensorFlow 2.15.0 |
| - Datasets 2.16.1 |
| - Tokenizers 0.15.0 |
| |
