| language: | |
| - ach | |
| - en | |
| tags: | |
| - translation | |
| license: cc-by-4.0 | |
| datasets: | |
| - JW300 | |
| metrics: | |
| - bleu | |
| # HEL-ACH-EN | |
| ## Model description | |
| MT model translating Acholi to English initialized with weights from [opus-mt-luo-en](https://huggingface.co/Helsinki-NLP/opus-mt-luo-en) on HuggingFace. | |
| ## Intended uses & limitations | |
| Machine Translation experiments. Do not use for sensitive tasks. | |
| #### How to use | |
| ```python | |
| # 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](https://huggingface.co/Helsinki-NLP/opus-mt-luo-en?text=Bed+gi+nyasi+mar+chieng%27+nyuol+mopong%27+gi+mor%21#model_card) | |
| ## Training procedure | |
| Remove duplicates and rows with no alphabetic characters. Used GPU | |
| ## Eval results | |
| testset | BLEU | |
| --- | --- | |
| JW300.luo.en| 46.1 | |