README.md

---
license: apache-2.0
language:
- en
- multilingual
tags:
- code-to-docstring
- code-summarization
- code-documentation
- encoder-decoder
- code
- python
- java
- transformers
- huggingface
- modernbert
- gpt2
base_model:
- Shuu12121/CodeModernBERT-Ghost
- openai-community/gpt2-large
pipeline_tag: text-generation
---

#　CodeEncoderDecoderModel-Ghost-large👻

A multilingual encoder-decoder model for generating **docstrings from code snippets**.  
It is based on a custom BERT-style encoder pretrained on source code (`CodeModernBERT-Ghost`) and a large-scale decoder model (`GPT2-large`).

## 🏗️ Model Architecture

- **Encoder:** [`Shuu12121/CodeModernBERT-Ghost`](https://huggingface.co/Shuu12121/CodeModernBERT-Ghost)  
- **Decoder:** [`openai-community/gpt2-large`](https://huggingface.co/openai-community/gpt2-large)  
- Connected via HuggingFace's `EncoderDecoderModel` with cross-attention.

## 🎯 Intended Use

- Generating docstrings (documentation comments) for functions or methods in multiple languages.
- Summarizing code for educational or review purposes.
- Assisting in automated documentation generation pipelines.

Supported languages (code input):
- Python
- Java

## 📦 How to Use

```python
from transformers import AutoTokenizer, EncoderDecoderModel
import torch

model = EncoderDecoderModel.from_pretrained("Shuu12121/CodeEncoderDecoderModel-Ghost-large").to("cuda")
encoder_tokenizer = AutoTokenizer.from_pretrained("Shuu12121/CodeEncoderDecoderModel-Ghost-large", subfolder="encoder_tokenizer")
decoder_tokenizer = AutoTokenizer.from_pretrained("Shuu12121/CodeEncoderDecoderModel-Ghost-large", subfolder="decoder_tokenizer")

if decoder_tokenizer.pad_token is None:
    decoder_tokenizer.pad_token = decoder_tokenizer.eos_token

code = '''
def greet(name):
    return f"Hello, {name}!"
'''

inputs = encoder_tokenizer(code, return_tensors="pt", truncation=True, padding=True, max_length=2048).to("cuda")
outputs = model.generate(
    input_ids=inputs.input_ids,
    attention_mask=inputs.attention_mask,
    max_length=256,
    num_beams=5,
    early_stopping=True,
    decoder_start_token_id=model.config.decoder_start_token_id,
    eos_token_id=model.config.eos_token_id,
    pad_token_id=model.config.pad_token_id,
    no_repeat_ngram_size=2
)

docstring = decoder_tokenizer.decode(outputs[0], skip_special_tokens=True)
print(docstring)
```

## 🧪 Training Details

- **Task:** Code-to-docstring generation
- **Dataset:** [CodeXGLUE: Code-to-Text](https://github.com/microsoft/CodeXGLUE) – using subsets of Python, Java, JavaScript, Go, Ruby, PHP
- **Loss:** Cross-entropy loss over tokenized docstrings
- **Max input length:** 2048 (encoder), max output length: 256 (decoder)
- **Decoder modifications:** Adapted GPT2-large with padding and cross-attention

## ⚠️ Limitations & Risks

1. **Generated documentation may be inaccurate, incomplete, or misleading**. Always review generated docstrings manually.
2. **Formatting may not follow specific standards** (e.g., Google/Numpy style in Python or full Javadoc).
3. **Limited context:** Only considers single-function input; lacks broader project-level understanding.
4. **Language variance:** Performance may differ depending on the programming language due to data distribution.
5. **⚠️ Decoder risks (GPT2-large):**  
   GPT-2 models are known to sometimes generate inappropriate, offensive, or biased outputs, depending on the prompt.  
   Although this model is fine-tuned on technical datasets (code-docstring pairs), due to inherited properties from `gpt2-large`, similar risks **may still be present** in edge cases. Please exercise caution, especially when using the model in public or educational settings.

## 📄 License

Apache-2.0  
Model weights and tokenizer artifacts are released under the same license. You are free to use, modify, and redistribute with attribution.