Update README.md

README.md

@@ -1,202 +1,96 @@
 ---
 base_model: meta-llama/Llama-3.2-3B-Instruct
 library_name: peft
+language:
+- ko
+- en
+metrics:
+- accuracy
+pipeline_tag: text-classification
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **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]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- llama3.2-3B 모델을 prompt를 고정하고 lora 방식으로 학습한 모델입니다.
+- 기쁨, 당황, 분노, 불안, 상처, 슬픔 총 6가지 감정을 학습하였습니다.
+- 데이터는 AIHUB의 [감성 대화 말뭉치](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=data&dataSetSn=86)를 사용했습니다.
+- 나이와 성별도 학습시 사용했습니다.
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[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]
+```
+import re
+import torch
+from transformers import AutoTokenizer
+from peft import AutoPeftModelForCausalLM
 
-#### Hardware
+model = None
+tokenizer = None
+device = None
 
-[More Information Needed]
+PROMPT="""<|prompt|>You are an AI assistant tasked with analyzing the emotional content of a diary entry. Your goal is to determine the most closely matching emotion from a predefined list.
 
-#### Software
+Here is the diary entry you need to analyze:
 
-[More Information Needed]
+<diary_entry>
+age: {age} | gender: {gender} | diary: {sentence}
+</diary_entry>
 
-## Citation [optional]
+Please carefully read and analyze the content of this diary entry. Consider the overall tone, the events described, and the language used by the writer.
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+Based on your analysis, choose the emotion that best matches the overall sentiment of the diary entry from the following list:
 
-**BibTeX:**
+['분노', '불안', '상처', '슬픔', '당황', '기쁨']
 
-[More Information Needed]
+Translate these emotions to English for your understanding:
+['분노(anger)', '불안(anxiety)', '상처(hurt)', '슬픔(sadness)', '당황(embarrassment)', '기쁨(happiness)']
 
-**APA:**
+After you've made your decision, respond with only the chosen emotion in Korean. Do not provide any explanation or additional text.
 
-[More Information Needed]
+Your response should be formatted as follows:
+<emotion>[chosen emotion in korean]</emotion>
 
-## Glossary [optional]
+Once you've provided the emotion, end the conversation. Do not engage in any further dialogue or provide any additional information.
+<|assistant|>"""
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+def load_model():
+    global model, tokenizer, device
 
-[More Information Needed]
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    path = './llama-3.2-3B-sentiment_241105'
+    
+    tokenizer = AutoTokenizer.from_pretrained(path)
+    model = AutoPeftModelForCausalLM.from_pretrained(
+        path,
+        attn_implementation="flash_attention_2",
+        torch_dtype=torch.float16,
+        device_map=device,
+    )
+    model.eval()
 
-## More Information [optional]
+def generate(text, age, gender):
+    global model, tokenizer, device
+    text = PROMPT.format(age=age, gender=gender, sentence=text)
+    inputs = tokenizer(text, return_tensors="pt").to(device)
 
-[More Information Needed]
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_new_tokens=11, pad_token_id=tokenizer.pad_token_id)
+        decoded_output = tokenizer.decode(outputs[0])
 
-## Model Card Authors [optional]
+        try:
+            pred = decoded_output.split("<|assistant|>")[1]
+            pred = re.search(r'<emotion>(.*?)</emotion>', pred).group(1)
+        except:
+            pred = 'error'
+            
+    return pred
 
-[More Information Needed]
+print(generate("오늘 친구랑 싸웠어.", "", ""))
+```
 
-## Model Card Contact
+## Accuracy
+데이터 학습시 일부를 테스트용 데이터로 정확도 측정 결과 약 70%를 달성했습니다.
 
-[More Information Needed]
 ### Framework versions
 
 - PEFT 0.13.0