hwanmin/lecture-midm-7B-food-order-understanding

Model Card for Model ID

실험 내용

• 영화에 대한 리뷰와 그 리뷰의 긍정, 부정에 대한 정보가 있는 nsmc 데이터셋을 가지고 midm모델을 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

midm의 정확도 : 0.893

Metric	Value
PP (True Positive)	422
PN (True Negative)	471
TP (False Positive)	43
TN (False Negative)	64

학습데이터 상위1000개의 샘플을 가지고 테스트한 결과

• PP : 긍정 예측이면서 정답도 긍정인 경우 : 422
• PN : 부정 예측이면서 정답도 부정인 경우 : 471
• TP : 긍정 예측이면서 정답은 부정인 경우 : 43
• TN : 부정 예측이면서 정답은 긍정인 경우 : 64
• midm의 정확도 : 0.893

Model Details

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Training Details

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Training procedure

The following bitsandbytes quantization config was used during training:

• quant_method: bitsandbytes
• load_in_8bit: False
• load_in_4bit: True
• llm_int8_threshold: 6.0
• llm_int8_skip_modules: None
• llm_int8_enable_fp32_cpu_offload: False
• llm_int8_has_fp16_weight: False
• bnb_4bit_quant_type: nf4
• bnb_4bit_use_double_quant: False
• bnb_4bit_compute_dtype: bfloat16

Framework versions

• PEFT 0.7.0