# Machine Translation with Opus-MT

*author: David Wang*

<br />

## Description

A machine translation operator translates a sentence, paragraph, or document from source language 
to the target language. This operator is trained on [OPUS](https://opus.nlpl.eu/) data by Helsinki-NLP. 
More detail can be found in [ Helsinki-NLP/Opus-MT ](https://github.com/Helsinki-NLP/Opus-MT). 

<br />

## Code Example

Use the pre-trained model 'opus-mt-en-zh'
to generate the Chinese translation for the sentence "Hello, world.".

*Write the pipeline*:

```python
import towhee

(
    towhee.dc(["Hello, world."])
          .machine_translation.opus_mt(model_name="opus-mt-en-zh")
)
```

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

```python
import towhee

(
    towhee.dc['text'](["Hello, world."])
          .machine_translation.opus_mt['text', 'vec'](model_name="opus-mt-en-zh")
          .show()
)
```

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

<br />

## Factory Constructor

Create the operator via the following factory method:

***machine_translatioin.opus_mt(model_name="opus-mt-en-zh")***

**Parameters:**

***model_name***: *str*

The model name in string.
The default model name is "opus-mt-en-zh".

Supported model names:
 - opus-mt-en-zh
 - opus-mt-zh-en

<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 translated text in string.

***__call__(text)***

**Parameters:**

***text***: *str*

​	The source language text in string.

**Returns**:

*str*

​	The target language text.