Translation
Transformers
Safetensors
English
marian
text2text-generation
ibani
english-to-ibani
low-resource
Instructions to use williampepple1/ibani-translator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use williampepple1/ibani-translator 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="williampepple1/ibani-translator")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("williampepple1/ibani-translator") model = AutoModelForSeq2SeqLM.from_pretrained("williampepple1/ibani-translator") - Notebooks
- Google Colab
- Kaggle
English to Ibani Translation Model
This model translates English text to Ibani language. It's fine-tuned from Helsinki-NLP/opus-mt-en-mul.
Language
Ibani is an indigenous language spoken primarily in Rivers State, Nigeria.
Due to the lack of an official ISO-639-3 code, this project uses the
custom language identifier ibani, consistently applied across the model
and dataset.
Usage
from transformers import MarianMTModel, MarianTokenizer
model = MarianMTModel.from_pretrained("your-username/ibani-translator")
tokenizer = MarianTokenizer.from_pretrained("your-username/ibani-translator")
text = "I eat fish"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs)
translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(translation)
Training Data
Trained on English-Ibani parallel sentences from the ibani_eng dataset.
Performance
[Add your evaluation metrics here]
Model Details
- Base Model: Helsinki-NLP/opus-mt-en-mul
- Language Pair: English โ Ibani
- Task: Machine Translation
- Framework: Hugging Face Transformers
Limitations
- This is a low-resource language model
- Performance may vary with complex sentence structures
- Best results with simple to moderate complexity sentences
Citation
If you use this model, please cite:
@misc{ibani-translator,
author = {Your Name},
title = {English to Ibani Translation Model},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/your-username/ibani-translator}}
}
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