---
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: lora-llama-2-7b-food-order-understanding
  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. -->

# <Llama2 모델을 nsmc 데이터셋을 해결하는 모델이 되도록 미세튜닝 하기>

모델: Llama2</br>
데이터셋: nsmc</br>
https://huggingface.co./datasets/nsmc </br>
Train 데이터: 3000</br>
Test 데이터: 1000

## [테스트 결과]

**정확도: 86.10%**

**혼동행렬(Confusion Matrix)**

||정답 Positive|정답 Negative|
|:------:|:------:|:------:|
|예측 Positive|395|26|
|예측 Negative|113|466|


**평가지표**

||||
|:------:|:------:|:------:|
|정밀도(Precision)|0.938|
|재현율(Recall)|0.459|
|F1 Score|0.616|

## [성능 향상] </br>
train 데이터 수를 2000에서 2500, 3000으로 늘려가며 성능을 약 11% 정도 높였으며,
TrainingArguments의 max_steps 등의 파라미터를 조절해가며 성능을 높이고자 노력하였다. 



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# lora-llama-2-7b-food-order-understanding

This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co./meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## 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



### Framework versions

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