metadata
language:
- ru
- bua
- bxr
datasets:
- SaranaAbidueva/buryat-russian_parallel_corpus
metrics:
- bleu
How to use in Python:
from transformers import MBartForConditionalGeneration, MBart50Tokenizer
model = MBartForConditionalGeneration.from_pretrained("SaranaAbidueva/mbart50_ru_bua")
tokenizer = MBart50Tokenizer.from_pretrained("SaranaAbidueva/mbart50_ru_bua")
def fix_tokenizer(tokenizer):
old_len = len(tokenizer) - int('bxr_XX' in tokenizer.added_tokens_encoder)
tokenizer.lang_code_to_id['bxr_XX'] = old_len-1
tokenizer.id_to_lang_code[old_len-1] = 'bxr_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 'bxr_XX' not in tokenizer._additional_special_tokens:
tokenizer._additional_special_tokens.append('bxr_XX')
tokenizer.added_tokens_encoder = {}
fix_tokenizer(tokenizer)
def translate(text, src='ru_RU', trg='bxr_XX', max_length=200, num_beams=5, repetition_penalty=5.0, **kwargs):
tokenizer.src_lang = src
encoded = tokenizer(text, return_tensors="pt")
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,
# early_stopping=True,
)
return tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
translate('Евгений Онегин интересная книга')