---
license: cc-by-nc-4.0
base_model: KT-AI/midm-bitext-S-7B-inst-v1
tags:
- generated_from_trainer
model-index:
- name: lora-midm-nsmc
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# lora-midm-nsmc

This model is a fine-tuned version of [KT-AI/midm-bitext-S-7B-inst-v1](https://huggingface.co./KT-AI/midm-bitext-S-7B-inst-v1) on an nsmc dataset.

## Model description

KT-midm model을 nsmc데이터를 활용하여 미세튜닝한 모델  
영화 리뷰 데이터를 기반으로 사용자가 작성한 리뷰의 긍정 또는 부정을 파악한다.  


## Intended uses & limitations

### Intended uses
사용자가 작성한 리뷰의 긍정 또는 부정 감정 분석을 제공함

### Limitaions
영화 리뷰에 특화되어 있으며, 다른 유형에는 제한이 있을 수 있음  
Colab T4 GPU에서 테스트 되었음

## Training and evaluation data

Training data: nsmc 'train' data 중 상위 2000개의 샘플  
Evaluation data: nsmc 'test' data 중 상위 1000개의 샘플

## 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 with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 300
- mixed_precision_training: Native AMP

### Training results


![image/png](https://cdn-uploads.huggingface.co/production/uploads/652384150f935fa8fd6c6779/jd7jtIHmniBqcYJ3tlEID.png)

TrainOutput(global_step=300, training_loss=1.1105608495076498,  
metrics={'train_runtime': 929.3252, 'train_samples_per_second': 0.646,  
'train_steps_per_second': 0.323, 'total_flos': 9315508499251200.0,  
'train_loss': 1.1105608495076498, 'epoch': 0.3})

### 정확도
Midm: 정확도 0.89  
|               | Positive Prediction(PP) | Negative Prediction(NP) |
|--------------------|---------------------|---------------------|
| True Positive (TP) | 474                 | 34                  |
| True Negative (TN) | 76                  | 416                 |

### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0