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---
license: cc-by-nc-4.0
language:
- ru
- tyv
pipeline_tag: translation
datasets:
- Agisight/tyvan-russian-parallel-50k
---
It is a [NLLB-200-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) model
fine-tuned for translating between Tyvan and Russian languages using the dataset from https://tyvan.ru.
Here is [a post](https://cointegrated.medium.com/a37fc706b865) about how it was trained.
How to use the model:
```Python
# the version of transformers is important!
!pip install sentencepiece transformers==4.33
import torch
from transformers import NllbTokenizer, AutoModelForSeq2SeqLM
def fix_tokenizer(tokenizer, new_lang='tyv_Cyrl'):
""" Add a new language token to the tokenizer vocabulary (this should be done each time after its initialization) """
old_len = len(tokenizer) - int(new_lang in tokenizer.added_tokens_encoder)
tokenizer.lang_code_to_id[new_lang] = old_len-1
tokenizer.id_to_lang_code[old_len-1] = new_lang
# always move "mask" to the last position
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 new_lang not in tokenizer._additional_special_tokens:
tokenizer._additional_special_tokens.append(new_lang)
# clear the added token encoder; otherwise a new token may end up there by mistake
tokenizer.added_tokens_encoder = {}
tokenizer.added_tokens_decoder = {}
MODEL_URL = "slone/nllb-rus-tyv-v1"
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_URL)
tokenizer = NllbTokenizer.from_pretrained(MODEL_URL)
fix_tokenizer(tokenizer)
def translate(
text,
model,
tokenizer,
src_lang='rus_Cyrl',
tgt_lang='tyv_Cyrl',
max_length='auto',
num_beams=4,
n_out=None,
**kwargs
):
tokenizer.src_lang = src_lang
encoded = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
if max_length == 'auto':
max_length = int(32 + 2.0 * encoded.input_ids.shape[1])
model.eval()
generated_tokens = model.generate(
**encoded.to(model.device),
forced_bos_token_id=tokenizer.lang_code_to_id[tgt_lang],
max_length=max_length,
num_beams=num_beams,
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
translate("красная птица", model=model, tokenizer=tokenizer)
# 'кызыл куш'
``` |