Update README.md

README.md

@@ -9,7 +9,7 @@ Pretrained model on the Tswana language using a masked language modeling (MLM) o
 TswanaBERT is a transformer model pre-trained on a corpus of Setswana in a self-supervised fashion by masking part of the input words and training to predict the masks by using byte-level tokens.
 
 ## Intended uses & limitations
-The model can  be used for either masked language modeling or next word prediction. It can also be fine-tuned on a specific down-stream NLP application. 
+The model can  be used for either masked language modeling or next-word prediction. It can also be fine-tuned on a specific downstream NLP application. 
 
 #### How to use
 
@@ -45,15 +45,15 @@ The model can  be used for either masked language modeling or next word predicti
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 #### Limitations and bias
-The model is trained on a relatively small collection of setwana, mostly from news articles and creative writtings, and so is not representative enough of the language as yet.
+The model is trained on a relatively small collection of sestwana, mostly from news articles and creative writings, and so is not representative enough of the language as yet.
 
 ## Training data
 
 1. The largest portion of this dataset (10k)  sentences of text, comes from the [Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download)
 
-2. I Then added SABC news headlines collected by Marivate Vukosi, & Sefara Tshephisho, (2020)  that is generously made available on [zenoodo](http://doi.org/10.5281/zenodo.3668495 ). This added 185 tswana sentences to my corpus. 
+2. We then added SABC news headlines collected by Marivate Vukosi, & Sefara Tshephisho, (2020)  that are generously made available on [zenoodo](http://doi.org/10.5281/zenodo.3668495 ). This added 185 tswana sentences to my corpus. 
 
-3. I went on to add 300 more sentences by scrapping following news sites and blogs that mosty originate in Botswana. I actively continue to expand the dataset.
+3. We went on to add 300 more sentences by scrapping following news sites and blogs that mostly originate in Botswana. We actively continue to expand the dataset.
 
 * http://setswana.blogspot.com/
 * https://omniglot.com/writing/tswana.php