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MedAware

This model is a fine-tuned version of euclaise/falcon_1b_stage2 on MedQuAD: Medical Question Answering 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:

  • learning_rate: 0.0002
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_ratio: 0.05
  • num_epochs: 1

Training results

Training steps: 1025/1025; time- 2:45:21

Steps Training_loss
500 1.273800
1000 1.133000

TrainOutput(global_step=1025, training_loss=1.1994486794820647, metrics={'train_runtime': 9932.0844, 'train_samples_per_second': 1.652, 'train_steps_per_second': 0.103, 'total_flos': 3.2484758758785024e+16, 'train_loss': 1.1994486794820647, 'epoch': 1.0})

Framework versions

  • Transformers 4.30.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.12.0
  • Tokenizers 0.13.3
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Dataset used to train keivalya/MedAware