updating yoruba readme

README.md

@@ -37,17 +37,17 @@ 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 Bible, JW300, [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), [Yoruba Embedding corpus](https://huggingface.co/datasets/yoruba_text_c3) and [CC-Aligned](https://opus.nlpl.eu/), Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends.
+This model was fine-tuned on Bible, JW300, [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt), [Yoruba Embedding corpus(https://huggingface.co/datasets/yoruba_text_c3) and [CC-Aligned](https://opus.nlpl.eu/), Wikipedia, news corpora (BBC Yoruba, VON Yoruba, Asejere, Alaroye), and other small datasets curated from friends.
 
 ## Training procedure
 This model was trained on a single NVIDIA V100 GPU
 
-## Eval results on Test set (F-score)
-Dataset|F1-score
--|-
-Yoruba GV NER |75.34
-MasakhaNER |80.82
-BBC Yoruba |80.66
+## Eval results on Test set (F-score, average over 5 runs)
+Dataset| mBERT F1 | yo_bert F1
+-|-|-
+[Yorùbá GV NER](https://huggingface.co/datasets/yoruba_gv_ner) | | 
+[MasakhaNER](https://github.com/masakhane-io/masakhane-ner) | 78.97 | 
+[BBC Yorùbá Textclass](https://huggingface.co/datasets/yoruba_bbc_topics) | 75.13 | 79.11 
 
 ### BibTeX entry and citation info
 By David Adelani