---
language:
- myv
- ru
- fi
- de
- es
- en
- hi
- zh
- tr
- uk
- fr
- ar
tags:
- erzya
- mordovian
- fill-mask
- pretraining
- embeddings
- masked-lm
- feature-extraction
- sentence-similarity
license: cc-by-sa-4.0
datasets:
  - slone/myv_ru_2022
  - yhavinga/ccmatrix
---

This a model to translate texts from the Erzya language (`myv`, cyrillic script) to 11 other languages: `ru,fi,de,es,en,hi,zh,tr,uk,fr,ar`.

It is described in the paper "The first neural machine translation system for the Erzya language".

This model is based on [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) ([license here](https://tfhub.dev/google/LaBSE/2)), but with updated vocabulary and checkpoint:
- Added an extra language token `myv_XX` and 19K new BPE tokens for the Erzya language;
- Fine-tuned to translate to Erzya: first from Russian, then from all 11 languages. 

The following code can be used to run translation using the model 

```Python
from transformers import MBartForConditionalGeneration, MBart50Tokenizer


def fix_tokenizer(tokenizer):
    """ Add a new language token to the tokenizer vocabulary (this should be done each time after its initialization) """
    old_len = len(tokenizer) - int('myv_XX' in tokenizer.added_tokens_encoder)
    tokenizer.lang_code_to_id['myv_XX'] = old_len-1
    tokenizer.id_to_lang_code[old_len-1] = 'myv_XX'
    tokenizer.fairseq_tokens_to_ids["<mask>"] = len(tokenizer.sp_model) + len(tokenizer.lang_code_to_id) + tokenizer.fairseq_offset

    tokenizer.fairseq_tokens_to_ids.update(tokenizer.lang_code_to_id)
    tokenizer.fairseq_ids_to_tokens = {v: k for k, v in tokenizer.fairseq_tokens_to_ids.items()}
    if 'myv_XX' not in tokenizer._additional_special_tokens:
        tokenizer._additional_special_tokens.append('myv_XX')
    tokenizer.added_tokens_encoder = {}


def translate(text, model, tokenizer, src='ru_RU', trg='myv_XX', max_length='auto', num_beams=3, repetition_penalty=5.0, train_mode=False, n_out=None, **kwargs):
    tokenizer.src_lang = src
    encoded = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024)
    if max_length == 'auto':
        max_length = int(32 + 1.5 * encoded.input_ids.shape[1])
    if train_mode:
        model.train()
    else:
        model.eval()
    generated_tokens = model.generate(
        **encoded.to(model.device),
        forced_bos_token_id=tokenizer.lang_code_to_id[trg], 
        max_length=max_length, 
        num_beams=num_beams,
        repetition_penalty=repetition_penalty,
        num_return_sequences=n_out or 1,
        **kwargs
    )
    out = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
    if isinstance(text, str) and n_out is None:
        return out[0]
    return out
    

mname = 'slone/mbart-large-51-mul-myv-v1'
model = MBartForConditionalGeneration.from_pretrained(mname)
tokenizer = MBart50Tokenizer.from_pretrained(mname)
fix_tokenizer(tokenizer)


print(translate('Привет, собака!', model, tokenizer, src='ru_RU', trg='myv_XX'))
# Шумбрат, киска!  # действительно, по-эрзянски собака именно так
print(translate('Hello, doggy!', model, tokenizer, src='en_XX', trg='myv_XX'))
# Шумбрат, киска!
```