Model Description This model is a fine-tuned version of the bert-base-cased architecture, specifically designed for text classification tasks related to diagnosing diseases from symptoms. The primary objective is to analyze natural language descriptions of symptoms and predict one of 22 corresponding diagnoses.

Dataset Information The model was trained on the Gretel/symptom_to_diagnosis dataset, which consists of 1,065 symptom descriptions in the English language, each labeled with one of the 22 possible diagnoses. The dataset focuses on fine-grained single-domain diagnosis, making it suitable for tasks that require detailed classification based on symptom descriptions. Example

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': 'AdamW', 'weight_decay': 0.004, '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': 2e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
training_precision: float32

Training results

Framework versions

Transformers 4.41.2
TensorFlow 2.15.0
Datasets 2.19.2
Tokenizers 0.19.1

AlperenEvci/bert-symptom-diagnosis

bert-symptom-diagnosis

Model description

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

AlperenEvci/
bert-symptom-diagnosis