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--- |
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license: apache-2.0 |
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base_model: google/mobilebert-uncased |
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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-mobile-bert-uncased |
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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-mobile-bert-uncased |
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This model is a fine-tuned version of [google/mobilebert-uncased](https://huggingface.co/google/mobilebert-uncased) on a MedMCQA dataset. |
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It achieves the following results on the evaluation set: |
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- Train Loss: 4.0974 |
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- Validation Loss: 1.5722 |
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- Train Accuracy: 0.244 |
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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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| 4852.5933 | 15.4345 | 0.245 | 0 | |
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| 168.1700 | 10.9270 | 0.27 | 1 | |
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| 103.5470 | 29.7673 | 0.243 | 2 | |
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| 95.7810 | 15.3191 | 0.268 | 3 | |
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| 62.5387 | 5.4845 | 0.262 | 4 | |
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| 43.1666 | 9.3354 | 0.239 | 5 | |
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| 32.7722 | 27.9195 | 0.24 | 6 | |
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| 25.1578 | 5.1954 | 0.228 | 7 | |
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| 12.7365 | 2.1180 | 0.219 | 8 | |
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| 4.0974 | 1.5722 | 0.244 | 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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