codebert/README.md

# Code & Text Embedding with CodeBert

*author: [Jael Gu](https://github.com/jaelgu)*

<br />

## Description

A code search operator takes a text string of programming language or natural language as an input
and returns an embedding vector in ndarray which captures the input's core semantic elements.
This operator is implemented with pre-trained models from [Huggingface Transformers](https://huggingface.co/docs/transformers).

<br />

## Code Example

Use the pre-trained model "huggingface/CodeBERTa-small-v1"
to generate text embeddings for given 
text description "return max value" and code "def max(a,b): if a>b: return a else return b".

*Write the pipeline*:

```python
import towhee

(
    towhee.dc(['find max value', 'def max(a,b): if a>b: return a else return b'])
          .code_search.codebert(model_name='huggingface/CodeBERTa-small-v1')
)
```

*Write a same pipeline with explicit inputs/outputs name specifications:*

```python
import towhee

(
    towhee.dc['text'](['return max value', 'def max(a,b): if a>b: return a else return b'])
          .code_search.codebert['text', 'embedding']()
          .show()
)
```

<img src="./result.png" width="800px"/>

<br />

## Factory Constructor

Create the operator via the following factory method:

***code_search.codebert(model_name="huggingface/CodeBERTa-small-v1")***

**Parameters:**

***model_name***: *str*

The model name in string.
The default model name is "huggingface/CodeBERTa-small-v1".

***device***: *str*

The device to run model inference.
The default value is None, which enables GPU if cuda is available.

Supported model names:


<br />

## Interface

The operator takes a piece of text in string as input.
It loads tokenizer and pre-trained model using model name.
and then return an embedding in ndarray.

***__call__(txt)***

**Parameters:**

***txt***: *str*

​	The text string in programming language or natural language.


**Returns**:

*numpy.ndarray*

​	The text embedding generated by model, in shape of (dim,).


***save_model(format="pytorch", path="default")***

Save model to local with specified format.

**Parameters:**

***format***: *str*

​	The format of saved model, defaults to "pytorch".

***format***: *path*

​	The path where model is saved to. By default, it will save model to the operator directory.


***supported_model_names(format=None)***

Get a list of all supported model names or supported model names for specified model format.

**Parameters:**

***format***: *str*

​	The model format such as "pytorch", "torchscript".
The default value is None, which will return all supported model names.