metadata
license: apache-2.0
base_model: Intel/dynamic_tinybert
tags:
- generated_from_keras_callback
model-index:
- name: medmcqa-tiny-bert
results: []
medmcqa-tiny-bert
This model is a fine-tuned version of Intel/dynamic_tinybert on MedMCQA dataset. It achieves the following results on the evaluation set:
- Train Loss: 0.4325
- Validation Loss: 3.1445
- Train Accuracy: 0.293
- Epoch: 9
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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 5000, '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}
- training_precision: float32
Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|---|---|---|---|
| 1.3858 | 1.3862 | 0.329 | 0 |
| 1.3878 | 1.3850 | 0.321 | 1 |
| 1.3784 | 1.3869 | 0.318 | 2 |
| 1.3172 | 1.3945 | 0.33 | 3 |
| 1.1564 | 1.5962 | 0.307 | 4 |
| 0.9487 | 1.6876 | 0.295 | 5 |
| 0.7610 | 2.1023 | 0.29 | 6 |
| 0.6154 | 2.5488 | 0.289 | 7 |
| 0.5057 | 2.8837 | 0.292 | 8 |
| 0.4325 | 3.1445 | 0.293 | 9 |
Framework versions
- Transformers 4.37.2
- TensorFlow 2.15.0
- Datasets 2.17.1
- Tokenizers 0.15.2