Tips on how to Translate Languages with MarianMT and Hugging Face Transformers

Language translation has grow to be a necessary instrument in our more and more globalized world. Whether or not you are a developer, researcher, or traveler, you’ll all the time discover the necessity to talk with individuals from completely different cultures. Therefore, the flexibility to translate textual content rapidly and precisely might be very useful for you. One highly effective useful resource for attaining that is the MarianMT mannequin, part of the Hugging Face Transformers library.

On this information, we’ll stroll you thru the method of utilizing MarianMT to translate textual content between a number of languages, making it accessible even for these with minimal technical background.

What’s MarianMT?

MarianMT is a machine translation framework based mostly on the Transformer structure, which is well known for its effectiveness in pure language processing duties. Developed utilizing the Marian C++ library, the MarianMT fashions have an enormous benefit of being quick. Hugging Face has included MarianMT into their Transformers library, making it simpler to entry and use by means of Python.

Step-by-Step Information to Use MarianMT

1. Set up

To start, it’s essential to set up the mandatory libraries. Guarantee you could have Python put in in your system, then run the next command to put in the Hugging Face Transformers library:

You’ll additionally want the torch library for dealing with the mannequin’s computations:

2. Selecting a Mannequin

MarianMT fashions are pre-trained on numerous language pairs. The fashions comply with a naming conference of Helsinki-NLP/opus-mt-{src}-{tgt} in hugging face, the place {src} and {tgt} are the supply and goal language codes, respectively. For instance, when you search Helsinki-NLP/opus-mt-en-fr in hugging face, the corresponding mannequin would translate from English to French.

3. Loading the Mannequin and Tokenizer

Let’s say you resolve to translate English to a particular language, i.e., French. Then you definately would wish to load the appropriate mannequin and its corresponding tokenizer. Right here’s the way you load the mannequin and tokenizer:

from transformers import MarianMTModel, MarianTokenizer

# Specify the mannequin identify
model_name = "Helsinki-NLP/opus-mt-en-fr"

# Load the tokenizer and mannequin
tokenizer = MarianTokenizer.from_pretrained(model_name)
mannequin = MarianMTModel.from_pretrained(model_name)

4. Translating Textual content

Now that you’ve your mannequin and tokenizer prepared, you’ll be able to translate textual content in simply 4 easy steps! Right here’s a primary instance.To start with, you’ll specify the supply textual content in a variable that you simply wish to translate.

# Outline the supply textual content
src_text = ["this is a sentence in English that we want to translate to French"]

Since transformers (or any machine studying mannequin) doesn’t perceive textual content, we wish to convert the supply textual content into numeric type. For that, we’d tokenize our textual content. For a radical understanding of the best way to do tokenization, you’ll be able to check with my Tokenization article.

# Tokenize the supply textual content
inputs = tokenizer(src_text, return_tensors="pt", padding=True)

Then we’ll cross the tokenized sentence to the mannequin and it’ll output some numbers.

# Generate the interpretation
translated = mannequin.generate(**inputs)

Discover that mannequin outputs tokens, and never textual content straight. We must decode these tokens again to textual content so people can perceive the translated output of the mannequin.

# Decode the translated textual content
tgt_text = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]
print(tgt_text)

Within the above code, the output would be the translated textual content in French:

c'est une phrase en anglais que nous voulons traduire en français

5. Translating to A number of Languages

If you wish to translate English textual content into a number of languages, you should use multilingual fashions. For instance, the mannequin Helsinki-NLP/opus-mt-en-ROMANCE can translate english to a number of Romance languages (French, Portuguese, Spanish, and so forth.). Specify the goal language by prepending the supply textual content with the goal language code:

Output would appear to be this:

["c'est une phrase en anglais que nous voulons traduire en français",
 'Isto deve ir para o português.',
 'Y esto al español']

With this setup, you’ll be able to simply translate your English textual content into French, Portuguese, and Spanish. There are some teams of languages apart from ROMANCE languages as nicely. Here’s a checklist of them:

GROUP_MEMBERS = {
 'ZH': ['cmn', 'cn', 'yue', 'ze_zh', 'zh_cn', 'zh_CN', 'zh_HK', 'zh_tw', 'zh_TW', 'zh_yue', 'zhs', 'zht', 'zh'],
 'ROMANCE': ['fr', 'fr_BE', 'fr_CA', 'fr_FR', 'wa', 'frp', 'oc', 'ca', 'rm', 'lld', 'fur', 'lij', 'lmo', 'es', 'es_AR', 'es_CL', 'es_CO', 'es_CR', 'es_DO', 'es_EC', 'es_ES', 'es_GT', 'es_HN', 'es_MX', 'es_NI', 'es_PA', 'es_PE', 'es_PR', 'es_SV', 'es_UY', 'es_VE', 'pt', 'pt_br', 'pt_BR', 'pt_PT', 'gl', 'lad', 'an', 'mwl', 'it', 'it_IT', 'co', 'nap', 'scn', 'vec', 'sc', 'ro', 'la'],
 'NORTH_EU': ['de', 'nl', 'fy', 'af', 'da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
 'SCANDINAVIA': ['da', 'fo', 'is', 'no', 'nb', 'nn', 'sv'],
 'SAMI': ['se', 'sma', 'smj', 'smn', 'sms'],
 'NORWAY': ['nb_NO', 'nb', 'nn_NO', 'nn', 'nog', 'no_nb', 'no'],
 'CELTIC': ['ga', 'cy', 'br', 'gd', 'kw', 'gv']
}

Wrapping Up

Utilizing MarianMT fashions with the Hugging Face Transformers library supplies a robust and versatile approach to carry out language translations. Whether or not you’re translating textual content for private use, analysis, or integrating translation capabilities into your functions, MarianMT affords a dependable and easy-to-use resolution. With the steps outlined on this information, you may get began with translating languages effectively and successfully.
 
 

Kanwal Mehreen Kanwal is a machine studying engineer and a technical author with a profound ardour for knowledge science and the intersection of AI with medication. She co-authored the book “Maximizing Productivity with ChatGPT”. As a Google Technology Scholar 2022 for APAC, she champions variety and educational excellence. She’s additionally acknowledged as a Teradata Variety in Tech Scholar, Mitacs Globalink Analysis Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having based FEMCodes to empower girls in STEM fields.