---
language:
- code
- en
task_categories:
- text-classification
tags:
- arxiv:2305.06156
license: mit
metrics:
- accuracy
widget:
- text: |-
    Sum two integers</s></s>def sum(a, b):
        return a + b
  example_title: Simple toy
- text: |-
    Look for methods that might be dynamically defined and define them for lookup.</s></s>def respond_to_missing?(name, include_private = false)
      if name == :to_ary || name == :empty?
        false
      else
        return true if mapping(name).present?
        mounting = all_mountings.find{ |mount| mount.respond_to?(name) }
        return false if mounting.nil?
      end
    end
  example_title: Ruby example
- text: |-
    Method that adds a candidate to the party @param c the candidate that will be added to the party</s></s>public void addCandidate(Candidate c)
    {
        this.votes += c.getVotes(); 
        candidates.add(c); 
    }
  example_title: Java example
- text: |-
    we do not need Buffer pollyfill for now</s></s>function(str){
      var ret = new Array(str.length), len = str.length;
      while(len--) ret[len] = str.charCodeAt(len);
      return Uint8Array.from(ret);
    }
  example_title: JavaScript example
  
pipeline_tag: text-classification
---



## Table of Contents
- [Model Description](#model-description)
- [Model Details](#model-details)
- [Usage](#usage)
- [Limitations](#limitations)
- [Additional Information](#additional-information)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)


## Model Description

This model is trained based on [Codebert](https://github.com/microsoft/CodeBERT) and a 5M subset of [The Vault](https://huggingface.co/datasets/Fsoft-AIC/thevault-function-level) to detect the inconsistency between docstring/comment and function. It is used to remove noise examples in The Vault dataset.

More information:
- **Repository:** [FSoft-AI4Code/TheVault](https://github.com/FSoft-AI4Code/TheVault)
- **Paper:** [The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation](https://arxiv.org/abs/2305.06156)
- **Contact:** support.ailab@fpt.com


## Model Details
* Developed by: [Fsoft AI Center](https://www.fpt-aicenter.com/ai-residency/)
* License: MIT
* Model type: Transformer-Encoder based Language Model
* Architecture: BERT-base
* Data set: [The Vault](https://huggingface.co/datasets/Fsoft-AIC/thevault-function-level)
* Tokenizer: Byte Pair Encoding
* Vocabulary Size: 50265
* Sequence Length: 512
* Language: English and 10 Programming languages (Python, Java, JavaScript, PHP, C#, C, C++, Go, Rust, Ruby)
* Training details:
  * Self-supervised learning, binary classification
  * Positive class: Original code-docstring pair
  * Negative class: Random pairing code and docstring

## Usage
The input to the model follows the below template:
```python
"""
Template:
<s>{docstring}</s></s>{code}</s>

Example:
from transformers import AutoTokenizer

#Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("Fsoft-AIC/Codebert-docstring-inconsistency")

input = "<s>Sum two integers</s></s>def sum(a, b):\n    return a + b</s>"
tokenized_input = tokenizer(input, add_special_tokens= False)
"""
```

Using model with Jax
```python
from transformers import AutoTokenizer, FlaxAutoModelForSequenceClassification

#Load jax model
model = FlaxAutoModelForSequenceClassification.from_pretrained("Fsoft-AIC/Codebert-docstring-inconsistency")
```

Using model with Pytorch
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

#Load torch model
model = AutoModelForSequenceClassification.from_pretrained("Fsoft-AIC/Codebert-docstring-inconsistency")
```

## Limitations
This model is trained on a subset of 5M data in The Vault in the self-supervised manner. Since the negative samples are generated artificially, the model's ability to identify instances that require a strong semantic understanding between the code and the docstring might be restricted.

It is hard to evaluate the model due to the unavailable labeled datasets. ChatGPT is adopted as a reference to measure the correlation between the model and ChatGPT's scores. However, the result could be influenced by ChatGPT's potential biases and ambiguous conditions. Therefore, we recommend having human labeling dataset and finetune this model to achieve the best result.

## Additional information
### Licensing Information
[More information needed]

### Citation Information

```
@article{thevault,
  title={The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation},
  author={},
  journal={},
  pages={},
  year={2023}
}
```