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---
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
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