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---
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
|