license: mit
datasets:
- fka/awesome-chatgpt-prompts
language:
- en
metrics:
- bleu
base_model:
- openai/gpt-oss-20b
new_version: Qwen/Qwen3-235B-A22B-Instruct-2507
pipeline_tag: token-classification
library_name: espnet
tags:
- art
- code
- finance
Transformer-based Large Action Model for Code Understanding
Overview
This repository contains a PyTorch implementation of a Transformer-based model designed for understanding and generating code. The model learns rich representations of source code that can be used for tasks like code completion, code summarization, and code generation.
Key Features
- Complete Transformer Architecture: Implements both encoder and decoder with multi-head attention
- Positional Encoding: Captures sequential information in code
- Code-specific Dataset Handling: Preprocesses and batches code sequences
- Training Pipeline: Includes masked training and evaluation
- Code Generation: Can generate new code based on prompts
Model Architecture
The model follows the standard Transformer architecture with:
- Embedding layer with positional encoding
- Multiple encoder and decoder layers
- Multi-head attention mechanisms
- Position-wise feedforward networks
- Layer normalization and dropout
Requirements
Python 3.7+
PyTorch 1.8+
NumPy
Installation
git clone https://github.com/yourusername/code-transformer.git
cd code-transformer
pip install -r requirements.txt
Usage
Training the Model
from model import Transformer
from dataset import CodeDataset
from train import train_model
# Initialize model
model = Transformer(
src_vocab_size=10000,
tgt_vocab_size=10000,
d_model=512,
num_heads=8,
num_layers=6
)
# Prepare dataset
dataset = CodeDataset(your_code_sequences, max_len=100)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
# Train
train_model(model, dataloader, epochs=10)
Generating Code
from generate import generate_code
# Generate code from prompt
prompt = torch.tensor([your_start_tokens]) # Shape: [1, seq_len]
generated = generate_code(
model,
prompt,
max_len=100,
start_symbol=1, # Your start token
end_symbol=2 # Your end token
)
Data Preparation
Prepare your code data as sequences of tokens. The dataset should be:
Tokenized (using your preferred tokenizer)
Converted to numerical indices
Padded to consistent lengths
Example format:
[
[1, 45, 23, 67, 2], # First code sample
[1, 89, 12, 34, 56, 2], # Second code sample
...
]
Configuration
Here are the key hyperparameters you can configure:
| Parameter | Description | Recommended Value |
|---------------|-----------------------------|------------------|
| `d_model` | Embedding dimension | `256-1024` |
| `num_heads` | Attention heads | `4-16` |
| `num_layers` | Encoder/decoder layers | `4-12` |
| `d_ff` | Feedforward dimension | `2048-4096` |
| `dropout` | Dropout rate | `0.1-0.3` |
| `batch_size` | Training batch size | `16-64` |
Evaluation
The model can be evaluated on:
Code completion accuracy
Generation quality (BLEU score, etc.)
Downstream task performance
Pretrained Models
Coming soon! We plan to release pretrained models for:
Python code understanding
JavaScript code generation
Multi-language embeddings
Contributing
Contributions are welcome! Please open an issue or pull request for:
Bug fixes
Performance improvements
Additional features
License
MIT License
Citation
If you use this code in your research, please cite:
@misc{code-transformer,
author = {Your Name},
title = {Transformer-based Code Understanding Model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/yourusername/code-transformer}}
}