---
license: apache-2.0
tags:
- generated_from_keras_callback
- medical
base_model: distilbert/distilbert-base-cased
model-index:
- name: distilbert-finetuned-medical-diagnosis
  results:
  - task:
      type: text-classification
      name: Text classification
    dataset:
      type: ninaa510/diagnosis-text
      name: Symptoms and diseases for classification
      split: test
    metrics:
      - type: accuracy         # Required. Example: wer. Use metric id from https://hf.co/metrics
        value: 58.68       # Required. Example: 20.90
        name: Accuracy         # Optional. Example: Test WER

datasets:
- ninaa510/diagnosis-text
language:
- en
metrics:
- accuracy
pipeline_tag: text-classification

widget:
- text: "I have had a persistent cough for the last three days. The cough sometimes includes blood. I am also suffering from fatigue and  a loss of appetite."
---

<!-- 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. -->

# distilbert-finetuned-medical-diagnosis

This model is a fine-tuned version of [distilbert/distilbert-base-cased](https://huggingface.co/distilbert/distilbert-base-cased) on the dataset
[here](https://huggingface.co/ninaa510/diagnosis-text).

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