---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: medmcqa-distil-bert-based-uncased
  results: []
---

<!-- 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. -->

# medmcqa-distil-bert-based-uncased

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a MedMCQA dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1025
- Validation Loss: 4.4572
- Train Accuracy: 0.275
- Epoch: 9

## 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': None, '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': 5000, '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

| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.3835     | 1.3719          | 0.35           | 0     |
| 1.3589     | 1.3579          | 0.325          | 1     |
| 1.2628     | 1.4648          | 0.328          | 2     |
| 1.0069     | 1.5701          | 0.304          | 3     |
| 0.6441     | 2.3132          | 0.287          | 4     |
| 0.3951     | 2.8174          | 0.281          | 5     |
| 0.2386     | 3.6746          | 0.299          | 6     |
| 0.1708     | 4.0410          | 0.287          | 7     |
| 0.1358     | 4.2157          | 0.288          | 8     |
| 0.1025     | 4.4572          | 0.275          | 9     |


### Framework versions

- Transformers 4.37.2
- TensorFlow 2.15.0
- Datasets 2.17.1
- Tokenizers 0.15.2