# Translation

Translation is the task of converting text from one language to another.

Inputs
###### Input

My name is Omar and I live in Zürich.

Translation Model
Output
###### Output

Mein Name ist Omar und ich wohne in Zürich.

## Use Cases

You can find over a thousand Translation models on the Hub, but sometimes you might not find a model for the language pair you are interested in. When this happen, you can use a pretrained multilingual Translation model like mBART and further train it on your own data in a process called fine-tuning.

### Multilingual conversational agents

Translation models can be used to build conversational agents across different languages. This can be done in two ways.

• Translate the dataset to a new language. You can translate a dataset of intents (inputs) and responses to the target language. You can then train a new intent classification model with this new dataset. This allows you to proofread responses in the target language and have better control of the chatbot's outputs.
• Translate the input and output of the agent. You can use a Translation model in user inputs so that the chatbot can process it. You can then translate the output of the chatbot into the language of the user. This approach might be less reliable as the chatbot will generate responses that were not defined before.

## Inference

You can use the 🤗 Transformers library with the translation_xx_to_yy pattern where xx is the source language code and yy is the target language code. The default model for the pipeline is t5-base which under the hood adds a task prefix indicating the task itself, e.g. “translate: English to French”.

from transformers import pipeline
en_fr_translator = pipeline("translation_en_to_fr")
en_fr_translator("How old are you?")
## [{'translation_text': ' quel âge êtes-vous?'}]


If you’d like to use a specific model checkpoint that is from one specific language to another, you can also directly use the translation pipeline.

from transformers import pipeline

model_checkpoint = "Helsinki-NLP/opus-mt-en-fr"
translator = pipeline("translation", model=model_checkpoint)
translator("How are you?")
# [{'translation_text': 'Comment allez-vous ?'}]


## Compatible libraries

Transformers
Translation demo
using t5-small
Translation
Examples
Examples
This model can be loaded on the Inference API on-demand.
Models for Translation

Note A general-purpose Transformer that can be used to translate from English to German, French, or Romanian.

Datasets for Translation

Note A dataset of copyright-free books translated into 16 different languages.

Note An example of translation between programming languages. This dataset consists of functions in Java and C#.

Metrics for Translation
bleu
BLEU score is calculated by counting the number of shared single or subsequent tokens between the generated sequence and the reference. Subsequent n tokens are called “n-grams”. Unigram refers to a single token while bi-gram refers to token pairs and n-grams refer to n subsequent tokens. The score ranges from 0 to 1, where 1 means the translation perfectly matched and 0 did not match at all
sacrebleu
SacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores. Inspired by Rico Sennrich's multi-bleu-detok.perl, it produces the official WMT scores but works with plain text. It also knows all the standard test sets and handles downloading, processing, and tokenization for you. See the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.