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README.md

@@ -1,199 +1,106 @@
 ---
-library_name: transformers
-tags: []
+license: mit
+base_model: klue/bert-base
+tags:
+- bert
+- lora
+- korean
+- text-classification
+- sentiment-analysis
+language:
+- ko
+datasets:
+- nsmc
 ---
 
-# Model Card for Model ID
+# NSMC 감정 분석 (LoRA Fine-tuned)
 
-<!-- Provide a quick summary of what the model is/does. -->
+이 모델은 LoRA를 사용하여 NSMC(Naver Sentiment Movie Corpus) 데이터셋으로 파인튜닝된 감정 분석 모델입니다.
 
+## 모델 정보
 
+- **베이스 모델:** klue/bert-base
+- **파인튜닝 방법:** LoRA (Low-Rank Adaptation)
+- **학습 파라미터:** 약 0.3% (~300K)
+- **데이터셋:** NSMC (네이버 영화 리뷰)
+- **Task:** 이진 분류 (긍정/부정)
 
-## Model Details
+## 사용 방법
+```python
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from peft import PeftModel
+import torch
 
-### Model Description
+# 베이스 모델 로드
+base_model = AutoModelForSequenceClassification.from_pretrained(
+    "klue/bert-base",
+    num_labels=2
+)
 
-<!-- Provide a longer summary of what this model is. -->
+# LoRA 어댑터 로드
+model = PeftModel.from_pretrained(base_model, "JINIIII/nsmc-sentiment-lora")
+tokenizer = AutoTokenizer.from_pretrained("JINIIII/nsmc-sentiment-lora")
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+# 추론
+text = "이 영화 정말 재미있어요!"
 
-- **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]
+inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
+outputs = model(**inputs)
 
-### Model Sources [optional]
+probs = torch.softmax(outputs.logits, dim=-1)
+pred = torch.argmax(probs, dim=-1).item()
 
-<!-- Provide the basic links for the model. -->
+label = "긍정" if pred == 1 else "부정"
+confidence = probs[0][pred].item()
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+print(f"결과: {label} (확신도: {confidence:.2%})")
+```
 
-## Uses
+## 학습 세부사항
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+- **LoRA Rank (r):** 8
+- **LoRA Alpha:** 16
+- **Target Modules:** query, value
+- **Dropout:** 0.1
+- **학습 에폭:** 5
+- **학습률:** 5e-4
 
-### Direct Use
+## 성능
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+- **학습 데이터:** 10,000개
+- **평가 데이터:** 2,000개
+- **평가 정확도:** ~85-90%
 
-[More Information Needed]
+## 활용 예시
 
-### Downstream Use [optional]
+- 영화 리뷰 감정 분석
+- 상품 리뷰 분석
+- SNS 감정 모니터링
+- 고객 피드백 자동 분류
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+## 제한사항
 
-[More Information Needed]
+- 영화 리뷰 도메인에 특화되어 있음
+- 짧은 텍스트에서 가장 좋은 성능
+- 극단적인 감정 표현에서 정확도가 높음
 
-### Out-of-Scope Use
+## 라이선스
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+MIT License
 
-[More Information Needed]
+## 작성자
 
-## Bias, Risks, and Limitations
+JINIIII
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+## 인용
+```bibtex
+@misc{nsmc-sentiment-lora,
+  author = {JINIIII},
+  title = {NSMC Sentiment Analysis with LoRA},
+  year = {2024},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/JINIIII/nsmc-sentiment-lora}
+}
+```
 
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-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
-
-<!-- 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. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **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]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+**Note**: 이 모델은 교육 목적으로 만들어졌습니다.