Update README.md

README.md

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-<!-- This model card has been generated automatically according to the information Keras had access to. You should
-probably proofread and complete it, then remove this comment. -->
-
 # Symptom_to_Diagnosis
 
-This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/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](https://huggingface.co/bert-base-cased)
+on this dataset (https://huggingface.co/datasets/gretelai/symptom_to_diagnosis).
 
 ## Model description
 
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
+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. 
 
-## Training procedure
+## 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
 
-### Training hyperparameters
+{
+  "output_text": "drug reaction",
+  "input_text": "I've been having headaches and migraines, and I can't sleep. My whole body shakes and twitches. Sometimes I feel lightheaded."
+}
 
-The following hyperparameters were used during training:
-- 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': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
-- training_precision: float32
 
-### Training results