lora-llama-2-7b-nsmc

This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on 'nsmc' dataset.

Model description

[meta-llama/Llama-2-7b-chat-hf]๋ฅผ nsmc ๋ฐ์ดํ„ฐ์…‹์„ ์ด์šฉํ•˜์—ฌ ๋ฏธ์„ธํŠœ๋‹ํ•จ.

Intended uses & limitations

๋ชฉ์ : ์˜ํ™” ๋ฆฌ๋ทฐ ํŒ๋‹จ (๊ธ์ •/๋ถ€์ •)

Training and evaluation data

  • training data: nsmc์˜ train ๋ฐ์ดํ„ฐ ์ค‘ ์ƒ์œ„ 2000๊ฐœ
  • evaluation data: nsmc์˜ test ๋ฐ์ดํ„ฐ ์ค‘ ์ƒ์œ„ 1000๊ฐœ

Training hyperparameters

The following hyperparameters were used during training:

  • 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

TP TN
PP 436 36
PN 72 456
  • accuracy: 0.892
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