unixcoder base nine EU AI Act Compliance Profile

Model Card for UniXcoder-base

Model Details

Model Description

UniXcoder is a unified cross-modal pre-trained model that leverages multimodal data (i.e. code comment and AST) to pretrain code representation.

• Developed by: Microsoft Team
• Shared by [Optional]: Hugging Face
• Model type: Feature Engineering
• Language(s) (NLP): en
• License: Apache-2.0
• Related Models:
  • Parent Model: RoBERTa
• Resources for more information:
  • Associated Paper

Uses

1. Dependency

• pip install torch
• pip install transformers

2. Quick Tour

We implement a class to use UniXcoder and you can follow the code to build UniXcoder. You can download the class by

wget https://raw.githubusercontent.com/microsoft/CodeBERT/master/UniXcoder/unixcoder.py

import torch
from unixcoder import UniXcoder

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = UniXcoder("microsoft/unixcoder-base")
model.to(device)

In the following, we will give zero-shot examples for several tasks under different mode, including code search (encoder-only), code completion (decoder-only), function name prediction (encoder-decoder) , API recommendation (encoder-decoder), code summarization (encoder-decoder).

3. Encoder-only Mode

For encoder-only mode, we give an example of code search.

1) Code and NL Embeddings

Here, we give an example to obtain code fragment embedding from CodeBERT.

# Encode maximum function
func = "def f(a,b): if a>b: return a else return b"
tokens_ids = model.tokenize([func],max_length=512,mode="<encoder-only>")
source_ids = torch.tensor(tokens_ids).to(device)
tokens_embeddings,max_func_embedding = model(source_ids)

# Encode minimum function
func = "def f(a,b): if a<b: return a else return b"
tokens_ids = model.tokenize([func],max_length=512,mode="<encoder-only>")
source_ids = torch.tensor(tokens_ids)

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