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