lora-llama-2-7b-nsmc

Model description

Intended uses & limitations

Training and evaluation data

• training data: nsmc의 train 데이터 중 상위 2000개
• evaluation data: nsmc의 test 데이터 중 상위 1000개

Training hyperparameters

• learning_rate: 0.0001
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 2
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.03
• training_steps: 1000
• mixed_precision_training: Native AMP

Training results

• global_step=1000
• training_loss=0.7012106285095215
• metrics={'train_runtime': 3567.3706, 'train_samples_per_second': 0.561, 'train_steps_per_second': 0.28, 'total_flos': 4.0718328397824e+16, 'train_loss': 0.7012106285095215, 'epoch': 1.0}

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0

Accuracy

• accuracy: 0.892