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---
language: pcm
datasets:

---
# xlm-roberta-base-finetuned-naija
## Model description
**xlm-roberta-base-finetuned-naija** is a **Nigerian Pidgin RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Nigerian Pidgin language texts.  It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets.  

Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Nigerian Pidgin  corpus. 
## Intended uses & limitations
#### How to use
You can use this model with Transformers *pipeline* for masked token prediction.
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Davlan/xlm-roberta-base-finetuned-naija')
>>> unmasker("Another attack on ambulance happen for Koforidua in March <mask> year where robbers kill Ambulance driver")



```
#### 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 JW300 + [BBC Pidgin](https://www.bbc.com/pidgin)

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

## Eval results on Test set (F-score, average over 5 runs)
Dataset| XLM-R F1 | pcm_roberta F1
-|-|-
[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 87.26 | 90.00

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

```



{}
transformers pytorch xlm-roberta fill-mask autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us 

Hugging Face's logo
-------------------


language: pcm
datasets:




---


xlm-roberta-base-finetuned-naija
================================


Model description
-----------------


xlm-roberta-base-finetuned-naija is a Nigerian Pidgin RoBERTa model obtained by fine-tuning xlm-roberta-base model on Nigerian Pidgin language texts. It provides better performance than the XLM-RoBERTa on named entity recognition datasets.


Specifically, this model is a *xlm-roberta-base* model that was fine-tuned on Nigerian Pidgin corpus.


Intended uses & limitations
---------------------------


#### How to use


You can use this model with Transformers *pipeline* for masked token prediction.


#### 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 JW300 + BBC Pidgin


Training procedure
------------------


This model was trained on a single NVIDIA V100 GPU


Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------


Dataset: MasakhaNER, XLM-R F1: 87.26, pcm\_roberta F1: 90.00


### BibTeX entry and citation info


By David Adelani



#### How to use


You can use this model with Transformers *pipeline* for masked token prediction. #### 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 JW300 + BBC Pidgin


Training procedure
------------------


This model was trained on a single NVIDIA V100 GPU


Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------


Dataset: MasakhaNER, XLM-R F1: 87.26, pcm\_roberta F1: 90.00 ### BibTeX entry and citation info


By David Adelani
TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us 
 #### How to use


You can use this model with Transformers *pipeline* for masked token prediction. #### 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 JW300 + BBC Pidgin


Training procedure
------------------


This model was trained on a single NVIDIA V100 GPU


Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------


Dataset: MasakhaNER, XLM-R F1: 87.26, pcm\_roberta F1: 90.00 ### BibTeX entry and citation info


By David Adelani
passage: TAGS
#transformers #pytorch #xlm-roberta #fill-mask #autotrain_compatible #endpoints_compatible #region-us 
#### How to use


You can use this model with Transformers *pipeline* for masked token prediction.#### 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 JW300 + BBC Pidgin


Training procedure
------------------


This model was trained on a single NVIDIA V100 GPU


Eval results on Test set (F-score, average over 5 runs)
-------------------------------------------------------


Dataset: MasakhaNER, XLM-R F1: 87.26, pcm\_roberta F1: 90.00### BibTeX entry and citation info


By David Adelani