---
library_name: peft
base_model: KT-AI/midm-bitext-S-7B-inst-v1
---
# Accuracy
## 결과 분석

### 상위 1000개의 test dataset을 이용해 성능 확인

|               | TP    | TN   |
|:-------------:|:-----:|:----:|
| FP            | 452   | 41   | 
| FN            | 43    | 440  |




- **True Positive**: 452
- **True Negative**: 440
- **False Positive**: 41
- **False Negative**: 43
  
- **precision**: 0.9168356997971603
- **recall**: 0.9131313131313131
- **f1 score**: 0.9149797570850203


#### '긍정'과 '부정'에 포함되지 않은 단어로 예측한 경우

- **' ', '정'으로 예측**: 총 24개

### --> 오분류가 존재하지만, 정확도에 있어서 90% 이상의 성능을 보이고 있고, llama-2보다 우수한 성능을 보인다.
  
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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 0.6.2