Update README.md

README.md

@@ -1,3 +1,95 @@
 ---
 license: apache-2.0
+language:
+- ko
 ---
+<div align="center">
+  <h1>Korcen</h1>
+</div>
+
+![131_20220604170616](https://user-images.githubusercontent.com/85154556/171998341-9a7439c8-122f-4a9f-beb6-0e0b3aad05ed.png)
+
+korcen-ml은 기존 키워드 기반의 korcen의 우회가 쉽다는 단점을 극복하기위해 딥러닝을 통해 정확도를 한층 더 올리려는 프로젝트입니다.
+
+일부 모델만 공개하고 있으며 모델 파일은 [여기](https://github.com/KR-korcen/korcen-ml/tree/main/model)에서 확인이 가능합니다.
+
+더 많은 모델 파일과 학습 데이터를 다운받고 싶다면 문의주세요.
+
+|  | 데이터 문장수 |
+|------|------|
+| VDCNN(23.4.30) | 200,000개 |
+| VDCNN_KOGPT2(23.5.28) | 2,000,000개 |
+| VDCNN_LLAMA2(23.9.30) | 5,000,000개 | 
+| VDCNN_LLAMA2_V2(24.1.29) | 10,000,000개 |
+
+
+키워드 기반 기존 라이브러리 : [py version](https://github.com/KR-korcen/korcen), [ts version](https://github.com/KR-korcen/korcen.ts)
+
+[서포트 디스코드 서버](https://discord.gg/wyTU3ZQBPE)
+
+## 모델 검증
+데이터마다 욕설의 기준이 달라 오차가 있다는 걸 감안하고 확인하시기 바랍니다.
+
+
+|  | [korean-malicious-comments-dataset](https://github.com/ZIZUN/korean-malicious-comments-dataset) | [Curse-detection-data](https://github.com/2runo/Curse-detection-data) | [kmhas_korean_hate_speech](https://huggingface.co/datasets/jeanlee/kmhas_korean_hate_speech) | [Korean Extremist Website Womad Hate Speech Data](https://www.kaggle.com/datasets/captainnemo9292/korean-extremist-website-womad-hate-speech-data/data) |
+|------|------|------|------|------|
+| [korcen(v0.3.5)](https://github.com/KR-korcen/korcen) | 0.7121 | **0.8415** | 0.6800 | 0.6305 |
+| VDCNN(23.4.30) | 0.6900 | 0.4885 |  | 0.4885 |
+| VDCNN_KOGPT2(23.6.15) | 0.7545 | 0.7824 |  | 0.7055 |
+| VDCNN_LLAMA2(23.9.30) | 0.7762 | 0.8104 | 0.7296 | V2로 대체 |
+| VDCNN_LLAMA2_V2(24.1.29) | **0.8322** | 0.8410 | **0.7837** | **0.7120** |
+| [badword_check](https://github.com/Nam-SW/badword_check)(23.10.1) | 0.5829 | 0.6761 |  |  |
+| [CurseDetector](https://github.com/mangto/CurseDetector)(24.1.10) | 0.5679 | 시간소요로 테스트 블가 |  | 0.5785 |
+
+## example
+```py
+#py: 3.10, tf: 2.10
+#kogpt2
+import tensorflow as tf
+import numpy as np
+import pickle
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+maxlen = 1000
+
+model_path = 'vdcnn_model.h5'
+tokenizer_path = "tokenizer.pickle"
+
+model = tf.keras.models.load_model(model_path)
+with open(tokenizer_path, "rb") as f:
+    tokenizer = pickle.load(f)
+
+def preprocess_text(text):
+    text = text.lower()
+    
+    return text
+
+def predict_text(text):
+    sentence = preprocess_text(text)
+    encoded_sentence = tokenizer.encode_plus(sentence,
+                                             max_length=maxlen,
+                                             padding="max_length",
+                                             truncation=True)['input_ids']
+    sentence_seq = pad_sequences([encoded_sentence], maxlen=maxlen, truncating="post")
+    prediction = model.predict(sentence_seq)[0][0]
+    return prediction
+    
+while True:
+    text = input("Enter the sentence you want to test: ")
+    result = predict_text(text)
+    if result >= 0.5:
+        print("This sentence contains abusive language.")
+    else:
+        print("It's a normal sentence.")
+```
+
+
+## Maker
+
+
+>Tanat
+```
+github:   Tanat05
+discord:  Tanat05
+email:    tanat@tanat.kr
+```