Hugging Face's logo
---
language: 
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# mbart50-large-yor-eng-mt
## Model description
**mbart50-large-yor-eng-mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned   facebook/mbart-large-50 model.  It establishes a **strong baseline** for automatically translating texts from Yorùbá to English.  

Specifically, this model is a *mbart-large-50* model that was fine-tuned on  JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt). The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model. 

#### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains.  
## Training data
This model was fine-tuned on on  JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset

## Training procedure
This model was trained on NVIDIA V100 GPU

## Eval results on Test set (BLEU score)
Fine-tuning mbart50-large achieves **15.88 BLEU** on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 15.57

### BibTeX entry and citation info
By David Adelani
```

```



{}
transformers pytorch mbart text2text-generation arxiv:2103.08647 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #mbart #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #region-us 

Hugging Face's logo
---
language: 
- yo
- en
datasets:
- JW300 + Menyo-20k
---
# mbart50-large-yor-eng-mt
## Model description
mbart50-large-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned   facebook/mbart-large-50 model.  It establishes a strong baseline for automatically translating texts from Yorùbá to English.  

Specifically, this model is a *mbart-large-50* model that was fine-tuned on  JW300 Yorùbá corpus and Menyo-20k. The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model. 

#### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains.  
## Training data
This model was fine-tuned on on  JW300 corpus and Menyo-20k dataset

## Training procedure
This model was trained on NVIDIA V100 GPU

## Eval results on Test set (BLEU score)
Fine-tuning mbart50-large achieves 15.88 BLEU on Menyo-20k test set while mt5-base achieves 15.57

### BibTeX entry and citation info
By David Adelani




# mbart50-large-yor-eng-mt ## Model description
mbart50-large-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned   facebook/mbart-large-50 model.  It establishes a strong baseline for automatically translating texts from Yorùbá to English.  

Specifically, this model is a *mbart-large-50* model that was fine-tuned on  JW300 Yorùbá corpus and Menyo-20k. The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model. #### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data
This model was fine-tuned on on  JW300 corpus and Menyo-20k dataset ## Training procedure
This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score)
Fine-tuning mbart50-large achieves 15.88 BLEU on Menyo-20k test set while mt5-base achieves 15.57 ### BibTeX entry and citation info
By David Adelani
TAGS
#transformers #pytorch #mbart #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #region-us 
 # mbart50-large-yor-eng-mt ## Model description
mbart50-large-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned   facebook/mbart-large-50 model.  It establishes a strong baseline for automatically translating texts from Yorùbá to English.  

Specifically, this model is a *mbart-large-50* model that was fine-tuned on  JW300 Yorùbá corpus and Menyo-20k. The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model. #### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains. ## Training data
This model was fine-tuned on on  JW300 corpus and Menyo-20k dataset ## Training procedure
This model was trained on NVIDIA V100 GPU ## Eval results on Test set (BLEU score)
Fine-tuning mbart50-large achieves 15.88 BLEU on Menyo-20k test set while mt5-base achieves 15.57 ### BibTeX entry and citation info
By David Adelani
passage: TAGS
#transformers #pytorch #mbart #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #region-us 
# mbart50-large-yor-eng-mt## Model description
mbart50-large-yor-eng-mt is a machine translation model from Yorùbá language to English language based on a fine-tuned   facebook/mbart-large-50 model.  It establishes a strong baseline for automatically translating texts from Yorùbá to English.  

Specifically, this model is a *mbart-large-50* model that was fine-tuned on  JW300 Yorùbá corpus and Menyo-20k. The model was trained using Swahili(sw_KE) as the language since the pre-trained model does not initially support Yorùbá. Thus, you need to use the sw_KE for language code when evaluating the model.#### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains.## Training data
This model was fine-tuned on on  JW300 corpus and Menyo-20k dataset## Training procedure
This model was trained on NVIDIA V100 GPU## Eval results on Test set (BLEU score)
Fine-tuning mbart50-large achieves 15.88 BLEU on Menyo-20k test set while mt5-base achieves 15.57### BibTeX entry and citation info
By David Adelani