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---
license: mit
language:
- wo
- fr
metrics:
- bleu
pipeline_tag: translation
tags:
- text-generation-inference
---
# Model Documentation: Wolof to French Translation with NLLB-200
## Model Overview
This document describes a machine translation model fine-tuned from Meta's NLLB-200 for translating from Wolof to French. The model, hosted at `cifope/nllb-200-wo-fr-distilled-600M`, utilizes a distilled version of the NLLB-200 model which has been specifically optimized for translation tasks between the Wolof and French languages.
## Dependencies
The model requires the `transformers` library by Hugging Face. Ensure that you have the library installed:
```bash
pip install transformers
```
## Setup
Import necessary classes from the `transformers` library:
```python
from transformers import AutoModelForSeq2SeqLM, NllbTokenizer
```
Initialize the model and tokenizer:
```python
model = AutoModelForSeq2SeqLM.from_pretrained('cifope/nllb-200-wo-fr-distilled-600M')
tokenizer = NllbTokenizer.from_pretrained('facebook/nllb-200-distilled-600M')
```
## Translation Functions
### Translate from French to Wolof
The `translate` function translates text from French to Wolof:
```python
def translate(text, src_lang='fra_Latn', tgt_lang='wol_Latn', a=16, b=1.5, max_input_length=1024, **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]),
**kwargs
)
return tokenizer.batch_decode(result, skip_special_tokens=True)
```
### Translate from Wolof to French
The `reversed_translate` function translates text from Wolof to French:
```python
def reversed_translate(text, src_lang='wol_Latn', tgt_lang='fra_Latn', a=16, b=1.5, max_input_length=1024, **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]),
**kwargs
)
return tokenizer.batch_decode(result, skip_special_tokens=True)
```
## Usage
To use the model for translating text, simply call the `translate` or `reversed_translate` function with the appropriate text and parameters. For example:
```python
french_text = "L'argent peut être échangé à la seule banque des îles située à Stanley"
wolof_translation = translate(french_text)
print(wolof_translation)
wolof_text = "alkaati yi tàmbali nañu xàll léegi kilifa gi ñów"
french_translation = reversed_translate(wolof_text)
print(french_translation)
wolof_text = "alkaati yi tàmbali nañu xàll léegi kilifa gi ñów"
english_translation = reversed_translate(wolof_text,tgt_lang="eng_Latn")
print(english_translation)
```
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