Update README.md

README.md

@@ -5,203 +5,154 @@ datasets:
 - Shuu12121/java-codesearch-dataset-open
 language:
 - en
+- ja
 base_model:
 - Shuu12121/CodeHawks-ModernBERT-1.0
 pipeline_tag: sentence-similarity
 tags:
 - code
+- code-search
+- retrieval
+- sentence-similarity
+- bert
+- transformers
+- deep-learning
+- machine-learning
+- nlp
+- programming
+- multi-language
+- rust
+- python
+- java
+- javascript
+- php
+- ruby
+- go
 ---
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
 
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+# **CodeModernBERT-Owl**
 
-## Model Details
+## **概要 / Overview**
 
-### Model Description
+### **🦉 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**.  
 
-<!-- Provide a longer summary of what this model is. -->
+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**  
 
+---
 
-- **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]
-
-## 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
+## **📊 モデルパラメータ / Model Parameters**
+| パラメータ / Parameter   | 値 / Value |
+|-------------------------|------------|
+| **vocab_size**         | 50,000      |
+| **hidden_size**        | 768        |
+| **num_hidden_layers**  | 12         |
+| **num_attention_heads**| 12         |
+| **intermediate_size**  | 3,072      |
+| **max_position_embeddings** | 2,048 |
+| **type_vocab_size**    | 2          |
+| **hidden_dropout_prob**| 0.1        |
+| **attention_probs_dropout_prob** | 0.1 |
+| **local_attention_window** | 128 |
+| **rope_theta**         | 160,000    |
+| **local_attention_rope_theta** | 10,000 |
 
-[More Information Needed]
+---
 
-### Compute Infrastructure
+## **💻 モデルの使用方法 / How to Use**
+This model can be easily loaded using the **Hugging Face Transformers** library.  
 
-[More Information Needed]
+⚠️ **Requires `transformers >= 4.48.0`**  
 
-#### Hardware
+🔗 **[Colab Demo (Replace with "CodeModernBERT-Owl")](https://github.com/Shun0212/CodeBERTPretrained/blob/main/UseMyCodeMorph_ModernBERT.ipynb)**
 
-[More Information Needed]
+### **モデルのロード / Load the Model**
+```python
+from transformers import AutoModelForMaskedLM, AutoTokenizer
 
-#### Software
+tokenizer = AutoTokenizer.from_pretrained("Shuu12121/CodeModernBERT-Owl")
+model = AutoModelForMaskedLM.from_pretrained("Shuu12121/CodeModernBERT-Owl")
+```
 
-[More Information Needed]
+### **コード埋め込みの取得 / Get Code Embeddings**
+```python
+import torch
 
-## Citation [optional]
+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
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+embedding = get_embedding("def my_function(): pass", model, tokenizer)
+print(embedding.shape)
+```
 
-**BibTeX:**
+---
 
-[More Information Needed]
+# **🔍 評価結果 / Evaluation Results**
 
-**APA:**
+### **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`.**  
 
-[More Information Needed]
+---
 
-## Glossary [optional]
+## **📈 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  |
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+✅ **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.**  
 
-[More Information Needed]
+---
 
-## More Information [optional]
+## **📊 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  |
 
-[More Information Needed]
+---
 
-## Model Card Authors [optional]
+## **📝 結論 / Conclusion**
+✅ **Top performance in all languages**  
+✅ **Rust support successfully added through dataset augmentation**  
+✅ **Further performance improvements possible with better datasets**  
 
-[More Information Needed]
+---
 
-## Model Card Contact
+## **📜 ライセンス / License**
+📄 **Apache 2.0**  
 
-[More Information Needed]
+## **📧 連絡先 / Contact**
+📩 **For any questions, please contact:**  
+📧 **[email protected]**