bert-symptom-diagnosis
This model is a fine-tuned version of bert-base-cased on an unknown dataset. It achieves the following results on the evaluation set:
This model is a fine-tuned version of bert-base-cased on this dataset (https://huggingface.co/datasets/gretelai/symptom_to_diagnosis).
Test Loss: 0.2304, Test Accuracy: 0.9622
Model description
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
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Model tree for AlperenEvci/bert-symptom-diagnosis
Base model
google-bert/bert-base-cased