readme: ---
  license: mit
  
  language:
  - en
  
  widget:
    - text: "Let us translate some text from Livonian to Võro!"
  ---
  
  # NMT for Finno-Ugric Languages
  
  This is an NMT system for translating between Võro, Livonian, North Sami, South Sami as well as Estonian, Finnish, Latvian and English. It was created by fine-tuning Facebook's m2m100-418M on the liv4ever and smugri datasets.
  
  ## Tokenizer
  Four language codes were added to the tokenizer: __liv__, __vro__, __sma__ and __sme__. Currently the m2m100 tokenizer loads the list of languages from a hard-coded list, so it has to be updated after loading; see the code example below.
  
  ## Usage example
  Install the transformers and sentencepiece libraries: `pip install sentencepiece transformers`
  
  ```from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
  
  from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
  tokenizer = AutoTokenizer.from_pretrained("tartuNLP/m2m100_418M_smugri")
  #Fix the language codes in the tokenizer
  tokenizer.id_to_lang_token = dict(list(tokenizer.id_to_lang_token.items()) + list(tokenizer.added_tokens_decoder.items()))
  tokenizer.lang_token_to_id = dict(list(tokenizer.lang_token_to_id.items()) + list(tokenizer.added_tokens_encoder.items()))
  tokenizer.lang_code_to_token = { k.replace("_", ""): k for k in tokenizer.additional_special_tokens }
  tokenizer.lang_code_to_id = { k.replace("_", ""): v for k, v in tokenizer.lang_token_to_id.items() }
  
  model = AutoModelForSeq2SeqLM.from_pretrained("tartuNLP/m2m100_418M_smugri")
  
  tokenizer.src_lang = 'liv'
  
  encoded_src = tokenizer("Līvõ kēļ jelāb!", return_tensors="pt")
  
  encoded_out = model.generate(**encoded_src, forced_bos_token_id = tokenizer.get_lang_id("sme"))
  print(tokenizer.batch_decode(encoded_out, skip_special_tokens=True))
  ```
  
  The output is `Livčča giella eallá.`