Instructions to use Ogayo/Hel-ach-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ogayo/Hel-ach-en 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="Ogayo/Hel-ach-en")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Ogayo/Hel-ach-en") model = AutoModelForSeq2SeqLM.from_pretrained("Ogayo/Hel-ach-en") - Notebooks
- Google Colab
- Kaggle
HEL-ACH-EN
Model description
MT model translating Acholi to English initialized with weights from opus-mt-luo-en on HuggingFace.
Intended uses & limitations
Machine Translation experiments. Do not use for sensitive tasks.
How to use
# You can include sample code which will be formatted
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Ogayo/Hel-ach-en")
model = AutoModelForSeq2SeqLM.from_pretrained("Ogayo/Hel-ach-en")
Limitations and bias
Trained on Jehovah Witnesses data so contains theirs and Christian views.
Training data
Trained on OPUS JW300 data. Initialized with weights from opus-mt-luo-en
Training procedure
Remove duplicates and rows with no alphabetic characters. Used GPU
Eval results
| testset | BLEU |
|---|---|
| JW300.luo.en | 46.1 |
- Downloads last month
- 15