Update README.md

README.md

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 ## Model description
 
-`bert-base-uncased` finetuned on the unify-emotion-datasets (https://github.com/sarnthil/unify-emotion-datasets), then transferred to
-a small sample of 10K hand-tagged StockTwits messages. Optimized for extracting emotions from financial contexts.  
+`bert-base-uncased` finetuned on the unify-emotion-datasets (https://github.com/sarnthil/unify-emotion-datasets) [~250K texts with 7 labels -- neutral, happy, sad, anger, disgust, surprise, fear], then transferred to
+a small sample of 10K hand-tagged StockTwits messages. Optimized for extracting emotions from financial social media, such as StockTwits.  
 
 Sequence length 64, learning rate 2e-5, batch size 128, 8 epochs.