Model Card for Model ID
Model Details
llama2-7b모델 미세튜닝
네이버 영화 리뷰텍스트(NSMC)데이터셋을 프롬포트에 포함하여 모델에 입력하면
"긍정" 또는 "부정" 이라고 예측하는 텍스트 생성하는 것이 목표
실험내용: train dataset의 2000개 샘플,valid dataset의 1000개 샘플을 미세튜닝에 사용
- 일반적으로 1900스텝에서는 정확도 accuracy가 80후반대(약 85%)가 도출, 2000스텝이상부터 90%에 근접한 수치를 보였다.
- seq length=256 gradient accumulation steps=2로 설정하면 매우 낮은 정확도(accuracy)가 도출되기때문에 그 이상의
seq length와 accumulation steps가 필요하다는 사실 확인.
##Accuracy 정확도 분석
###valid_dataset(test dataset 1000개에 대한 정확도)
| TP | TN |
|---|
| PP | 460 | 48.00 |
| PN | 50 | 442.00 |
| Accuracy | | 0.902 |
***정확도:0.902
Model Description
- Developed by: [More Information Needed]
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]
Framework versions
