bert-char-ctc-en-ru-translit-v0

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This is a model for trainable transliteration from Latin (English but not only) to Russian Cyrillic

How to use:

import torch
from transformers import BertForMaskedLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("cointegrated/bert-char-ctc-en-ru-translit-v0", trust_remote_code=True)
model = BertForMaskedLM.from_pretrained("cointegrated/bert-char-ctc-en-ru-translit-v0")

text = 'Hello world! My name is David Dale, and yours is Schwarzenegger?'

with torch.inference_mode():
    batch = tokenizer(text, return_tensors='pt', spaces=1, padding=True).to(model.device)
    logits = torch.log_softmax(model(**batch).logits, axis=-1)
print(tokenizer.decode(logits[0].argmax(-1), skip_special_tokens=True))
# хэло Уорлд май нэйм из дэвид дэйл энд ёрз из скУорзэнэгжэр

The argument spaces could be from 0 to 4, and it affects the results; a recommended value is 2.

Why use:

• Just for fun
• To augment your training data, if for some reason you want to make it robust to script changes

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