updated model card

README.md

@@ -15,7 +15,7 @@ language:
 - multilingual
 
 ---
-# AfriBERTa_small
+# afriberta_small
 ## Model description
 AfriBERTa small is a pretrained multilingual language model with around 97 million parameters.
 The model has 4 layers, 6 attention heads, 768 hidden units and 3072 feed forward size.
@@ -33,13 +33,13 @@ For example, assuming we want to finetune this model on a token classification t
 >>> from transformers import AutoTokenizer, AutoModelForTokenClassification
 >>> model = AutoModelForTokenClassification.from_pretrained("castorini/afriberta_small")
 >>> tokenizer = AutoTokenizer.from_pretrained("castorini/afriberta_small")
-# we have to manually set the model max length because it is an imported sentencepiece model which hugginface does not properly support right now
+# we have to manually set the model max length because it is an imported trained sentencepiece model, which huggingface does not properly support right now
 >>> tokenizer.model_max_length = 512 
 ```
 
 #### Limitations and bias
-This model is possibly limited by its training dataset which are majorly obtained from news articles from a specific span of time. 
-Thus, it may not generalize well.
+- This model is possibly limited by its training dataset which are majorly obtained from news articles from a specific span of time. Thus, it may not generalize well.
+- This model is trained on very little data (less than 1 GB), hence it may not have seen enough data to learn very complex linguistic relations.
 
 ## Training data
 The model was trained on an aggregation of datasets from the BBC news website and Common Crawl.