Violet0203/hw-midm-7B-nsmc

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Model Details

네이버 영화 리뷰텍스트(NSMC)데이터셋을 프롬포트에 포함하여 모델에 입력하면

-- "긍정" 또는 "부정" 이라고 예측하는 텍스트 생성하는 것이 목표

실험내용: train dataset의 2100개 샘플,valid dataset의 1000개 샘플을 미세튜닝에 사용

• 일반적으로 1900스텝에서는 정확도 accuracy가 80후반대(약 85%)가 도출, 2000스텝이상부터 90%에 근접한 수치를 보였다.
• seq length를 312로 줄인 결과, seq length 384보다 훈련시간trainer.train이 적게 걸리지만 정확도도 감소
• gradient_accumulation steps을 2로 설정하여 미니배치를 통해 구해진 gradient값을 n step동안 global gradient에 누적시킨 후 한번에 업뎃->배치를 여러개 사용한 효과를 주는 등 노력함.

Accuracy 정확도 분석

valid_dataset(test dataset 1000개에 대한 정확도)

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	TP	TN
PP	438	70
PN	29	463
Accuracy	-	0.901

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***정확도:0.901

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Model Description

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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 0.7.1
• PEFT 0.7.0