update readme

README.md

@@ -1,21 +1,21 @@
 Hugging Face's logo
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-language: rw
+language: pcm
 datasets:
 
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-# xlm-roberta-base-finetuned-kinyarwanda
+# xlm-roberta-base-finetuned-naija
 ## Model description
-**xlm-roberta-base-finetuned-kinyarwanda** is a **Kinyarwanda RoBERTa** model obtained by fine-tuning **xlm-roberta-base** model on Kinyarwanda language texts.  It provides **better performance** than the XLM-RoBERTa on named entity recognition datasets.  
+**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 Kinyarwanda corpus. 
+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")
+>>> unmasker("Another attack on ambulance happen for Koforidua in March <mask> year where robbers kill Ambulance driver")
 
 
 
@@ -23,15 +23,15 @@ 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 + [KIRNEWS](https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus) + [BBC Gahuza](https://www.bbc.com/gahuza)
+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 | rw_roberta F1
+Dataset| XLM-R F1 | pcm_roberta F1
 -|-|-
-[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 73.22 | 77.76
+[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 87.26 | 90.00
 
 ### BibTeX entry and citation info
 By David Adelani