medmcqa-tiny-bert / README.md
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metadata
license: apache-2.0
base_model: Intel/dynamic_tinybert
tags:
  - generated_from_keras_callback
model-index:
  - name: medmcqa-tiny-bert
    results: []

medmcqa-tiny-bert

This model is a fine-tuned version of Intel/dynamic_tinybert on MedMCQA dataset. It achieves the following results on the evaluation set:

  • Train Loss: 0.4325
  • Validation Loss: 3.1445
  • Train Accuracy: 0.293
  • 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
1.3858 1.3862 0.329 0
1.3878 1.3850 0.321 1
1.3784 1.3869 0.318 2
1.3172 1.3945 0.33 3
1.1564 1.5962 0.307 4
0.9487 1.6876 0.295 5
0.7610 2.1023 0.29 6
0.6154 2.5488 0.289 7
0.5057 2.8837 0.292 8
0.4325 3.1445 0.293 9

Framework versions

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