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slone/
nllb-600M-azj-eng-v1

Name: nllb-600M-azj-eng-v1
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This is a version of NLLB fine-tuned to translate sentences between eng and azj languages, using the corresponding subset of https://github.com/turkic-interlingua/til-mt/tree/master/til_corpus.

Example inference code (with the correct NLLB preprocessing!):

Python

from transformers import NllbTokenizer, AutoModelForSeq2SeqLM, AutoConfig
# this code is adapted from the Stopes repo of the NLLB team
# https://github.com/facebookresearch/stopes/blob/main/stopes/pipelines/monolingual/monolingual_line_processor.py#L214

import re
import sys
import typing as tp
import unicodedata
from sacremoses import MosesPunctNormalizer


mpn = MosesPunctNormalizer(lang="en")
mpn.substitutions = [
    (re.compile(r), sub) for r, sub in mpn.substitutions
]


def get_non_printing_char_replacer(replace_by: str = " ") -> tp.Callable[[str], str]:
    non_printable_map = {
        ord(c): replace_by
        for c in (chr(i) for i in range(sys.maxunicode + 1))
        # same as \p{C} in perl
        # see https://www.unicode.org/reports/tr44/#General_Category_Values
        if unicodedata.category(c) in {"C", "Cc", "Cf", "Cs", "Co", "Cn"}
    }

    def replace_non_printing_char(line) -> str:
        return line.translate(non_printable_map)

    return replace_non_printing_char

replace_nonprint = get_non_printing_char_replacer(" ")

def preproc(text):
    clean = mpn.normalize(text)
    clean = replace_nonprint(clean)
    # replace 𝓕𝔯𝔞𝔫𝔠𝔢𝔰𝔠𝔞 by Francesca
    clean = unicodedata.normalize("NFKC", clean)
    return clean

# loading the model
model_name = "slone/nllb-600M-azj-eng-v1"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name).cuda()
tokenizer = NllbTokenizer.from_pretrained(model_name)

def translate(text, src_lang='eng_Latn', tgt_lang='azj_Latn', a=32, b=3, max_input_length=1024, num_beams=4, **kwargs):
    tokenizer.src_lang = src_lang
    tokenizer.tgt_lang = tgt_lang
    if isinstance(text, str):
        text = [text]
    text = [preproc(t) for t in text]
    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=max_input_length)
    result = model.generate(
        **inputs.to(model.device),
        forced_bos_token_id=tokenizer.convert_tokens_to_ids(tgt_lang),
        max_new_tokens=int(a + b * inputs.input_ids.shape[1]),
        num_beams=num_beams,
        **kwargs
    )
    return tokenizer.batch_decode(result, skip_special_tokens=True)

# Example of translating a couple of texts:
texts = translate(["To be, or not to be, that is the question.", "Hello, how are you?"],  src_lang='eng_Latn', tgt_lang='azj_Latn')
print(texts)
# ['Olmaq və ya olmamaq sualdır.', 'Salam, necə var?']

If you want to translate too many sentences, you will need to put them in small batches (batch size can be chosen as the largest that fits into your GPU memory). An efficient way would be to batch them by similar length, like below:

Python

def batched_translate(texts, batch_size=16, **kwargs):
    """Translate texts in batches of similar length"""
    idxs, texts2 = zip(*sorted(enumerate(texts), key=lambda p: len(p[1]), reverse=True))
    results = []
    for i in trange(0, len(texts2), batch_size):
        results.extend(translate(texts2[i: i+batch_size], **kwargs))
    return [p for i, p in sorted(zip(idxs, results))]

Please beware that for translating a longer text, you need to split it into sentences and process them individually.