safetensors region:us


# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->
euneeei/hw-midm-7B-nsmc


### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
- 한국어로 된 네이버 영화 리뷰 데이터셋입니다.

- ## train dataset : 3000개
- ## test dataset : 1000개



#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 learning_rate : 1e-4-> 2e-4
 max_steps=500 설정
 warmup_steps=100 설정

 
[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->


|     | precision | recall | f1-score | support|
|----|----|----|-------|------|
negative| 0.87 | 0.95 | 091 | 492
positive | 0.94 | 0.87 | 0.90 | 508
accuracy | | | 0.91 | 1000
macro avg | 0.91 | 0.91 | 0.91 | 1000
weighted avg | 0.91 | 0.91 | 0.91 | 1000

- ### confusion metrics

### [[ 466, 26 ]
### [68, 440]]

[More Information Needed]

### Results
- ## **정확도 0.51 -> 0.91로 높아졌습니다**


## 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