Update README.md

README.md

@@ -27,27 +27,15 @@ tags:
 - php
 - ruby
 - go
+new_version: Shuu12121/CodeModernBERT-Owl-v3-len2048
 ---
 
 # **🦉CodeModernBERT-Owl**
 
-## **概要 / Overview**
+旧版のモデルです。[こちらのモデル](!Shuu12121/CodeModernBERT-Owl-v3-len2048)など最新版をお使いください！！
 
-### **🦉 CodeModernBERT-Owl: 高精度なコード検索 & コード理解モデル**  
-**CodeModernBERT-Owl** is a **pretrained model** designed from scratch for **code search and code understanding tasks**.  
 
-Compared to previous versions such as **CodeHawks-ModernBERT** and **CodeMorph-ModernBERT**, this model **now supports Rust** and **improves search accuracy** in Python, PHP, Java, JavaScript, Go, and Ruby.  
-
-### **🛠 主な特徴 / Key Features**
-✅ **Supports long sequences up to 2048 tokens** (compared to Microsoft's 512-token models)  
-✅ **Optimized for code search, code understanding, and code clone detection**  
-✅ **Fine-tuned on GitHub open-source repositories (Java, Rust)**  
-✅ **Achieves the highest accuracy among the CodeHawks/CodeMorph series**  
-✅ **Multi-language support**: **Python, PHP, Java, JavaScript, Go, Ruby, and Rust**  
-
----
-
-## **📊 モデルパラメータ / Model Parameters**
+## ** モデルパラメータ / Model Parameters**
 | パラメータ / Parameter   | 値 / Value |
 |-------------------------|------------|
 | **vocab_size**         | 50,004      |
@@ -65,109 +53,5 @@ Compared to previous versions such as **CodeHawks-ModernBERT** and **CodeMorph-M
 
 ---
 
-## **💻 モデルの使用方法 / How to Use**
-This model can be easily loaded using the **Hugging Face Transformers** library.  
-
-⚠️ **Requires transformers >= 4.48.0**  
-
-🔗 **[Colab Demo (Replace with "CodeModernBERT-Owl")](https://github.com/Shun0212/CodeBERTPretrained/blob/main/UseMyCodeMorph_ModernBERT.ipynb)**
-
-### **モデルのロード / Load the Model**
-```python
-from transformers import AutoModelForMaskedLM, AutoTokenizer
-tokenizer = AutoTokenizer.from_pretrained("Shuu12121/CodeModernBERT-Owl")
-model = AutoModelForMaskedLM.from_pretrained("Shuu12121/CodeModernBERT-Owl")
-```
-
-### **コード埋め込みの取得 / Get Code Embeddings**
-```python
-import torch
-def get_embedding(text, model, tokenizer, device="cuda"):
-    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
-    if "token_type_ids" in inputs:
-        inputs.pop("token_type_ids")
-    inputs = {k: v.to(device) for k, v in inputs.items()}
-    outputs = model.model(**inputs)
-    embedding = outputs.last_hidden_state[:, 0, :]
-    return embedding
-embedding = get_embedding("def my_function(): pass", model, tokenizer)
-print(embedding.shape)
-```
-
-
----
-
-# **🔍 評価結果 / Evaluation Results**
-
-### **データセット / Dataset**
-📌 **Tested on code_x_glue_ct_code_to_text with a candidate pool size of 100.**  
-📌 **Rust-specific evaluations were conducted using Shuu12121/rust-codesearch-dataset-open.**  
-
----
-
-## **📈 主要な評価指標の比較（同一シード値）/ Key Evaluation Metrics (Same Seed)**
-| 言語 / Language | **CodeModernBERT-Owl** | **CodeHawks-ModernBERT** | **Salesforce CodeT5+** | **Microsoft CodeBERT** | **GraphCodeBERT** |
-|-----------|-----------------|----------------------|-----------------|------------------|------------------|
-| **Python**     | **0.8793**  | 0.8551  | 0.8266  | 0.5243  | 0.5493  |
-| **Java**       | **0.8880**  | 0.7971  | **0.8867**  | 0.3134  | 0.5879  |
-| **JavaScript** | **0.8423**  | 0.7634  | 0.7628  | 0.2694  | 0.5051  |
-| **PHP**        | **0.9129**  | 0.8578  | **0.9027**  | 0.2642  | 0.6225  |
-| **Ruby**       | **0.8038**  | 0.7469  | **0.7568**  | 0.3318  | 0.5876  |
-| **Go**         | **0.9386**  | 0.9043  | 0.8117  | 0.3262  | 0.4243  |
-
-✅ **Achieves the highest accuracy in all target languages.**  
-✅ **Significantly improved Java accuracy using additional fine-tuned GitHub data.**  
-✅ **Outperforms previous models, especially in PHP and Go.**  
-
----
-
-## **📊 Rust (独自データセット) / Rust Performance**
-| 指標 / Metric | **CodeModernBERT-Owl** |
-|--------------|----------------|
-| **MRR**      | 0.7940 | 
-| **MAP**      | 0.7940 |
-| **R-Precision** | 0.7173 |
-
-### **📌 K別評価指標 / Evaluation Metrics by K**
-| K  | **Recall@K** | **Precision@K** | **NDCG@K** | **F1@K** | **Success Rate@K** | **Query Coverage@K** |
-|----|-------------|---------------|------------|--------|-----------------|-----------------|
-| **1**   | 0.7173  | 0.7173  | 0.7173  | 0.7173  | 0.7173  | 0.7173  |
-| **5**   | 0.8913  | 0.7852  | 0.8118  | 0.8132  | 0.8913  | 0.8913  |
-| **10**  | 0.9333  | 0.7908  | 0.8254  | 0.8230  | 0.9333  | 0.9333  |
-| **50**  | 0.9887  | 0.7938  | 0.8383  | 0.8288  | 0.9887  | 0.9887  |
-| **100** | 1.0000  | 0.7940  | 0.8401  | 0.8291  | 1.0000  | 1.0000  |
-
----
-
-## **🔁 別のおすすめモデル / Recommended Alternative Models**
-
-### 1. **CodeSearch-ModernBERT-Owl🦉** (https://huggingface.co/Shuu12121/CodeSearch-ModernBERT-Owl)  
-If you need a model that is **more specialized for code search**, this model is highly recommended.  
-コードサーチに**特化したモデルが必要な場合**はこちらがおすすめです。  
-
-### 2. **CodeModernBERT-Snake🐍** (https://huggingface.co/Shuu12121/CodeModernBERT-Snake)  
-If you need a pretrained model that supports **longer sequences or a smaller model size**, this model is ideal.  
-**シーケンス長が長い**、または**モデルサイズが小さい**事前学習済みモデルが必要な場合はこちらをおすすめします。  
-- **Maximum Sequence Length:** 8192 tokens  
-- **Smaller Model Size:** ~75M parameters  
-
-### 3. **CodeSearch-ModernBERT-Snake🐍** (https://huggingface.co/Shuu12121/CodeSearch-ModernBERT-Snake)  
-For those looking for a model that combines **long sequence length and code search specialization**, this model is the best choice.  
-**コードサーチに特化しつつ長いシーケンスを処理できるモデル**が欲しい場合にはこちらがおすすめです。  
-- **Maximum Sequence Length:** 8192 tokens  
-- **High Code Search Performance**
-
-
-## **📝 結論 / Conclusion**
-✅ **Top performance in all languages**  
-✅ **Rust support successfully added through dataset augmentation**  
-✅ **Further performance improvements possible with better datasets**  
-
----
-
-## **📜 ライセンス / License**
-📄 **Apache-2.0**  
-
-## **📧 連絡先 / Contact**
-📩 **For any questions, please contact:**  
-📧 **shun0212114@outlook.jp**
+## **ライセンス / License**
+**Apache-2.0**