Instructions to use osidenna/nllb-eng-ha-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use osidenna/nllb-eng-ha-v0 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="osidenna/nllb-eng-ha-v0")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("osidenna/nllb-eng-ha-v0") model = AutoModelForSeq2SeqLM.from_pretrained("osidenna/nllb-eng-ha-v0") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
This model is a fine-tuned version of facebook/nllb-200-distilled-600M for machine translation between English (eng) and Hassaniya Arabic (ha-ar).
It was trained on a dataset of English ↔ Hassaniya sentences.
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model_name = "kelSidenna/nllb-eng-ha-v0"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
text = "How are you?"
tokenizer.src_lang = "en"
inputs = tokenizer(text, return_tensors="pt")
translated_tokens = model.generate(
**inputs,
forced_bos_token_id=tokenizer.convert_tokens_to_ids("ha-ar")
)
print(tokenizer.batch_decode(translated_tokens, skip_special_tokens=True))
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Base model
facebook/nllb-200-distilled-600M