---
license: cc-by-nc-4.0
language:
- de
- frr
pipeline_tag: translation
base_model: facebook/nllb-200-distilled-600M
inference: false
---

# Northern Frisian translation model
This is an [NLLB-200-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) model fine-tuned for translating between German and 
the Northern Frisian dialect Mooring following [this great blogpost](https://cointegrated.medium.com/a37fc706b865).

## Data
The dataset for finetuning consisted of 7194 sentence pairs of the Ååstermooring dialect of North Frisian with German translation.
Most examples (roughly 5100) were taken directly from 
["Rüm Hart"](https://www.nordfriiskfutuur.eu/fileadmin/Content/Nordfriisk_Futuur/E-Books/N._A._Johannsen__Ruem_hart.pdf) 
published by the Nordfriisk Instituut. For sentence splitting the python 
[sentence-splitting library](https://pypi.org/project/sentence-splitter/) was used. The splitting wasn't perfect, 
especially in cases of direct speech, so that manual re-alignment and further splitting was necessary.
A further roughly 2000 examples were taken from the Frasch Uurdebök, Friesisches Wörterbuch, Neumünster 1988.
Finally, a little under 180 very simple self-written examples were used as evaluation data set.

## Usage
How to use the model:
```python
!pip install transformers==4.33

from transformers import AutoModelForSeq2SeqLM, NllbTokenizer

def create_tokenizer_with_new_lang(model_id, new_lang):
    tokenizer = NllbTokenizer.from_pretrained(model_id)
    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 = {}

    return tokenizer

def translate(
    text,
    tokenizer,
    model,
    src_lang='frr_Latn',
    tgt_lang='deu_Latn',
    a=32,
    b=3,
    max_input_length=1024,
    num_beams=4,
    **kwargs
):
    tokenizer.src_lang = src_lang
    tokenizer.tgt_lang = tgt_lang
    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=max_input_length)
    result = model.generate(
        **inputs.to(model.device),
        forced_bos_token_id=tokenizer.convert_tokens_to_ids(tgt_lang),
        max_new_tokens=int(a + b * inputs.input_ids.shape[1]),
        num_beams=num_beams,
        **kwargs
    )
    return tokenizer.batch_decode(result, skip_special_tokens=True)

path = "CmdCody/nllb-deu-moo"
tokenizer = create_tokenizer_with_new_lang(path, 'frr_Latn')
model = AutoModelForSeq2SeqLM.from_pretrained(path)

translate("Momme booget önj Naibel", tokenizer=tokenizer, model=model)
```

## Training
The model was trained in a Google Colab notebook for 5000 steps and a batch size of 16 following the above mentioned blog post.

Metrics on the evaluation data set:

|           | Bleu  | ChrF++ |
|-----------|-------|--------|
| Frr -> De | 48.79 | 65.12  |
| De -> Frr | 47.56 | 65.03  |