Hugging Face's logo
---
language: yo
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# mT5_base_yoruba_adr
## Model description
**mT5_base_yoruba_adr** is a **automatic diacritics restoration** model for Yorùbá language based on a fine-tuned  mT5-base  model.  It achieves the **state-of-the-art performance** for adding the correct diacritics or tonal marks to Yorùbá texts.  

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 ADR.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.from_pretrained("")
model = AutoModelForTokenClassification.from_pretrained("")
nlp = pipeline("", model=model, tokenizer=tokenizer)
example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
ner_results = nlp(example)
print(ner_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 Yorùbá 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)
64.63 BLEU on [Global Voices test set](https://arxiv.org/abs/2003.10564)
70.27 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647)

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

```



{}
transformers pytorch mt5 text2text-generation arxiv:2003.10564 arxiv:2103.08647 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #mt5 #text2text-generation #arxiv-2003.10564 #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

Hugging Face's logo
---
language: yo
datasets:
- JW300 + Menyo-20k
---
# mT5_base_yoruba_adr
## Model description
mT5_base_yoruba_adr is a automatic diacritics restoration model for Yorùbá language based on a fine-tuned  mT5-base  model.  It achieves the state-of-the-art performance for adding the correct diacritics or tonal marks to Yorùbá texts.  

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 ADR.

#### 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 Yorùbá corpus and Menyo-20k dataset

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

## Eval results on Test set (BLEU score)
64.63 BLEU on Global Voices test set
70.27 BLEU on Menyo-20k test set

### BibTeX entry and citation info
By Jesujoba Alabi and David Adelani




# mT5_base_yoruba_adr ## Model description
mT5_base_yoruba_adr is a automatic diacritics restoration model for Yorùbá language based on a fine-tuned  mT5-base  model.  It achieves the state-of-the-art performance for adding the correct diacritics or tonal marks to Yorùbá texts.  

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 ADR. #### 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 Yorùbá corpus and Menyo-20k dataset ## Training procedure
This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (BLEU score)
64.63 BLEU on Global Voices test set
70.27 BLEU on Menyo-20k test set ### BibTeX entry and citation info
By Jesujoba Alabi and David Adelani
TAGS
#transformers #pytorch #mt5 #text2text-generation #arxiv-2003.10564 #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # mT5_base_yoruba_adr ## Model description
mT5_base_yoruba_adr is a automatic diacritics restoration model for Yorùbá language based on a fine-tuned  mT5-base  model.  It achieves the state-of-the-art performance for adding the correct diacritics or tonal marks to Yorùbá texts.  

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 ADR. #### 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 Yorùbá corpus and Menyo-20k dataset ## Training procedure
This model was trained on a single NVIDIA V100 GPU ## Eval results on Test set (BLEU score)
64.63 BLEU on Global Voices test set
70.27 BLEU on Menyo-20k test set ### BibTeX entry and citation info
By Jesujoba Alabi and David Adelani
passage: TAGS
#transformers #pytorch #mt5 #text2text-generation #arxiv-2003.10564 #arxiv-2103.08647 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# mT5_base_yoruba_adr## Model description
mT5_base_yoruba_adr is a automatic diacritics restoration model for Yorùbá language based on a fine-tuned  mT5-base  model.  It achieves the state-of-the-art performance for adding the correct diacritics or tonal marks to Yorùbá texts.  

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 ADR.#### 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 Yorùbá corpus and Menyo-20k dataset## Training procedure
This model was trained on a single NVIDIA V100 GPU## Eval results on Test set (BLEU score)
64.63 BLEU on Global Voices test set
70.27 BLEU on Menyo-20k test set### BibTeX entry and citation info
By Jesujoba Alabi and David Adelani