Hugging Face's logo
---
language: 
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# mT5_base_eng_yor_mt
## Model description
**mT5_base_yor_eng_mt** is a **machine translation** model from English language to Yorùbá language based on a fine-tuned  mT5-base  model.  It establishes a **strong baseline** for automatically translating texts from English to Yorùbá.  

Specifically, this model is a *mT5_base* model that was fine-tuned on  JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for MT.
```python
from transformers import MT5ForConditionalGeneration, T5Tokenizer

model = MT5ForConditionalGeneration.from_pretrained("Davlan/mt5_base_eng_yor_mt")
tokenizer = T5Tokenizer.from_pretrained("google/mt5-base")
input_string = "Where are you?"
inputs = tokenizer.encode(input_string, return_tensors="pt")
generated_tokens = model.generate(inputs)
results = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
print(results)
```
#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. 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 a single NVIDIA V100 GPU

## Eval results on Test set (BLEU score)
9.82 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647)

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

```



{}
transformers pytorch mt5 text2text-generation arxiv:2103.08647 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #mt5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

Hugging Face's logo
---
language: 
- yo
- en
datasets:
- JW300 + Menyo-20k
---
# mT5_base_eng_yor_mt
## Model description
mT5_base_yor_eng_mt is a machine translation model from English language to Yorùbá language based on a fine-tuned  mT5-base  model.  It establishes a strong baseline for automatically translating texts from English to Yorùbá.  

Specifically, this model is a *mT5_base* model that was fine-tuned on  JW300 Yorùbá corpus and Menyo-20k
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for MT.

#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. 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 a single NVIDIA V100 GPU

## Eval results on Test set (BLEU score)
9.82 BLEU on Menyo-20k test set

### BibTeX entry and citation info
By David Adelani




# mT5_base_eng_yor_mt ## Model description
mT5_base_yor_eng_mt is a machine translation model from English language to Yorùbá language based on a fine-tuned  mT5-base  model.  It establishes a strong baseline for automatically translating texts from English to Yorùbá.  

Specifically, this model is a *mT5_base* model that was fine-tuned on  JW300 Yorùbá corpus and Menyo-20k ## Intended uses & limitations #### How to use
You can use this model with Transformers *pipeline* for MT. #### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. 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 a single NVIDIA V100 GPU ## Eval results on Test set (BLEU score)
9.82 BLEU on Menyo-20k test set ### BibTeX entry and citation info
By David Adelani
TAGS
#transformers #pytorch #mt5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # mT5_base_eng_yor_mt ## Model description
mT5_base_yor_eng_mt is a machine translation model from English language to Yorùbá language based on a fine-tuned  mT5-base  model.  It establishes a strong baseline for automatically translating texts from English to Yorùbá.  

Specifically, this model is a *mT5_base* model that was fine-tuned on  JW300 Yorùbá corpus and Menyo-20k ## Intended uses & limitations #### How to use
You can use this model with Transformers *pipeline* for MT. #### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. 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 a single NVIDIA V100 GPU ## Eval results on Test set (BLEU score)
9.82 BLEU on Menyo-20k test set ### BibTeX entry and citation info
By David Adelani
passage: TAGS
#transformers #pytorch #mt5 #text2text-generation #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# mT5_base_eng_yor_mt## Model description
mT5_base_yor_eng_mt is a machine translation model from English language to Yorùbá language based on a fine-tuned  mT5-base  model.  It establishes a strong baseline for automatically translating texts from English to Yorùbá.  

Specifically, this model is a *mT5_base* model that was fine-tuned on  JW300 Yorùbá corpus and Menyo-20k## Intended uses & limitations#### How to use
You can use this model with Transformers *pipeline* for MT.#### Limitations and bias
This model is limited by its training dataset of entity-annotated news articles from a specific span of time. 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 a single NVIDIA V100 GPU## Eval results on Test set (BLEU score)
9.82 BLEU on Menyo-20k test set### BibTeX entry and citation info
By David Adelani