distilbert-finetuned-medical-diagnosis
This model is a fine-tuned version of distilbert/distilbert-base-cased on the dataset here.
It achieves an accuracy of 58.68% on the test set of the dataset.
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': 1.0, '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': 1663, '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
Framework versions
- Transformers 4.41.0
- TensorFlow 2.15.0
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for ninaa510/distilbert-finetuned-medical-diagnosis
Base model
distilbert/distilbert-base-casedDataset used to train ninaa510/distilbert-finetuned-medical-diagnosis
Evaluation results
- Accuracy on Symptoms and diseases for classificationtest set self-reported58.680