
# 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

{"language": ["ach", "en"], "license": "cc-by-4.0", "tags": ["translation"], "datasets": ["JW300"], "metrics": ["bleu"]}
transformers pytorch marian text2text-generation translation ach en dataset:JW300 license:cc-by-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #marian #text2text-generation #translation #ach #en #dataset-JW300 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 

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


#### 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
------------




#### How to use #### 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
------------
TAGS
#transformers #pytorch #marian #text2text-generation #translation #ach #en #dataset-JW300 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 #### How to use #### 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
------------
passage: TAGS
#transformers #pytorch #marian #text2text-generation #translation #ach #en #dataset-JW300 #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #region-us 
#### How to use#### 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
------------