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hwanmin/lecture-llama-2-7B-food-order-understanding

sourceHugging Faceupdated 3y agoView on Hugging Face
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실험 내용

  • —영화에 대한 리뷰와 그 리뷰의 긍정, 부정에 대한 정보가 있는 nsmc 데이터셋을 가지고 Llama2모델을 미세튜닝하였다.
  • —train하기 위한 train데이터셋은 상위 2000개의 샘플을 사용하였다.
  • —test하기 위한 test데이터셋은 valid dataset으로 정의하였고 상위1000개의 샘플만 테스트 하였다.
  • —이때 테스트는 하나의 리뷰마다 테스트 해야하므로 ConstatntLengthDataset구조를 적용하지 않고 샘플을 추출하였다.

Model Evaluation Metrics

  • —Llama2: 정확도 0.821 | Metric | Value | |-----------------------|-------| | PP (True Positive) | 464 | | PN (True Negative) | 357 | | TP (False Positive) | 157 | | TN (False Negative) | 22 |

테스트 데이터에 대한 분류 결과

  • —학습데이터 상위1000개의 샘플을 가지고 테스트한 결과
  • —PP : 긍정 예측이면서 정답도 긍정인 경우 : 464
  • —PN : 부정 예측이면서 정답도 부정인 경우 : 357
  • —TP : 긍정 예측이면서 정답은 부정인 경우 : 157
  • —TN : 부정 예측이면서 정답은 긍정인 경우 : 22
  • —Llama2의 정확도 : 0.821

Model Details

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Training procedure

The following bitsandbytes quantization config was used during training:

  • —quant_method: bitsandbytes
  • —loadin8bit: False
  • —loadin4bit: True
  • —llmint8threshold: 6.0
  • —llmint8skip_modules: None
  • —llmint8enablefp32cpu_offload: False
  • —llmint8hasfp16weight: False
  • —bnb4bitquant_type: nf4
  • —bnb4bitusedoublequant: False
  • —bnb4bitcompute_dtype: bfloat16

Framework versions

  • —PEFT 0.7.0