Update README.md

README.md

@@ -1,11 +1,29 @@
 ---
-license: openrail
+language: twi
+license: mit
 ---
+## 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 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. 
 
 
 
-Limitations:
-The model was trained on a very small data (about 5MB), which is very limiting. 
+## 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. 
+
+
+## How to use it
+
+You can finetune 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
+>>> from transformers import AutoTokenizer, AutoModelForTokenClassification
+>>> model = AutoModelForTokenClassification.from_pretrained("sakrah/twibert")
+>>> tokenizer = AutoTokenizer.from_pretrained("sakrah/twibert")
+```