| --- |
| language: |
| - sw |
| - ha |
| - zu |
| - ki |
| - fr |
| - en |
| tags: |
| - gender-bias |
| - seq2seq |
| - text-generation |
| - african-nlp |
| - bias-correction |
| license: apache-2.0 |
| --- |
| |
| # JuaKazi Multilingual Bias Corrector v1 |
|
|
| Seq2seq gender bias correction model covering 6 languages. |
| Fine-tuned from `castorini/afriteva_v2_base` on ~10K correction pairs. |
|
|
| ## Usage |
|
|
| Input format: `correct bias {lang}: {biased sentence}` |
|
|
| Where `lang` is one of: `sw`, `ha`, `zu`, `ki`, `fr`, `en` |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
| |
| tokenizer = AutoTokenizer.from_pretrained("juakazike/multilingual-bias-corrector-v1") |
| model = AutoModelForSeq2SeqLM.from_pretrained("juakazike/multilingual-bias-corrector-v1") |
| |
| def correct(text, lang): |
| prompt = f"correct bias {lang}: {text}" |
| inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=128) |
| out = model.generate(**inputs, max_new_tokens=128, num_beams=4) |
| return tokenizer.decode(out[0], skip_special_tokens=True) |
| |
| correct("The chairman will lead the board meeting.", "en") |
| # -> "The chair will lead the board meeting." |
| ``` |
|
|
| ## Validation BLEU (val set, 10% held out per language) |
|
|
| | Language | Pairs | BLEU | |
| |----------|-------|------| |
| | Swahili (sw) | 1,586 | 17.7 | |
| | Hausa (ha) | 1,917 | 4.1 | |
| | Zulu (zu) | 1,931 | 0.6 | |
| | Gikuyu (ki) | 867 | 4.0 | |
| | French (fr) | 636 | 30.8 | |
| | English (en) | 3,464 | 38.6 | |
|
|
