Update README.md

README.md

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 ## TwiBERT
 ## Model Description
-TwiBERT is a language model pretrained on the Twi language, the most spoken language in Ghana, West Africa.
-The model has 61 million parameters, 6 attention heads, 768 hidden units and 3072 feed forward size. The model 
-was trained on the Asanti Twi Bible together with a crowdsourced dataset. 
+TwiBERT is a pre-trained language model specifically designed for the Twi language, which is widely spoken in Ghana, 
+West Africa. This model boasts 61 million parameters, 6 attention heads, 768 hidden units, and a feed-forward size of 3072. 
+To optimize its performance, TwiBERT was trained using a combination of the Asanti Twi Bible and a dataset 
+sourced through crowdsourcing efforts. 
 
 
 
 ## Limitations:
 
-The model was trained on a very small dataset (about 5MB), which makes it difficult for the model 
-to learn complex contextual embeddings  that will enable it to generalize. Plus, the scope of the dataset (the bible) might 
-give it strong religious bias. 
+The model was trained on a relatively limited dataset (approximately 5MB), 
+which may hinder its ability to learn intricate contextual embeddings and effectively generalize. 
+Additionally, the dataset's focus on the Bible could potentially introduce a strong religious bias in the model's output. 
 
 
 ## How to use it
 
-You can finetune TwiBERT by finetuning it on a downtream task. 
+You can use TwiBERT by finetuning it on a downtream task. 
 The example code below illustrates how you can use the TwiBERT model on a downtream task:
 
 ```python