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README.md

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 This destilbert model was fine-tuned on 50.000 stock news articles using the HuggingFace adapter from Kern AI refinery. The articles consisted of the headlines plus abstract of the article.
 For the finetuning, a single NVidia K80 was used for about four hours. 
 DistilBERT is a smaller, faster and lighter version of BERT. It was trained by distilling BERT base and has 40% less parameters than bert-base-uncased. 
-It runs 60% faster while preserving over 95% of BERT’s performances as measured on the GLUE language understanding benchmark 1. 
-DistilBERT does not have token-type embeddings, pooler and retains only half of the layers from Google’s BERT 2
+It runs 60% faster while preserving over 95% of BERT’s performances as measured on the GLUE language understanding benchmark. 
+DistilBERT does not have token-type embeddings, pooler and retains only half of the layers from Google’s BERT.
 ## Features
 
 - The model can handle various text classification tasks, especially when it comes to stock and finance news sentiment classification.