Model Card for Model ID
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
- 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
