peft safetensors dataset:nsmc base_model:KT-AI/midm-bitext-S-7B-inst-v1 region:us
---
library_name: peft
base_model: KT-AI/midm-bitext-S-7B-inst-v1
datasets:
- nsmc
---

### Model Description

- NSMC 데이터에 대해 KT-AI/midm-bitext-S-7B-inst-v1 미세튜닝
- 영화 리뷰 텍스트를 프롬프트에 포함하여 모델에 입력하면 '긍정' 또는 '부정'이라고 예측 텍스트를 직접 생성
- NSMC의 train 스플릿 상위 2,000개 이상의 샘플을 학습에 사용
- test 스플릿 상위 1,000개의 샘플만 측정


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: adam_beta1=0.9, adam_beta2=0.999, adam_epsilon=1e-08,
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_args.logging_steps: 50
- training_args.max_steps : 300
- trainable params: 16,744,448 || all params: 7,034,347,520 || trainable%: 0.23803839591934178


### Training Results

TrainOutput(
global_step=300, training_loss=2.666887741088867, 
metrics={'train_runtime': 961.226,
'train_samples_per_second': 0.624,
'train_steps_per_second': 0.312, 
'total_flos': 9315508499251200.0, 
'train_loss': 2.666887741088867, 'epoch': 0.3})



### Accuracy

Midm: 정확도 0.88

|  | TP | TN |
|---|---|---|
| PP | 416 | 23 |
| PN | 92 | 469 |



### Model Card Authors

cxoijve