Update README.md

README.md

@@ -15,17 +15,18 @@ metrics:
 - f1
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-# DistilBERT-Base-Uncased-Emotion
+# EmTract
+## DistilBERT-Base-Uncased-Emotion
 
 ## Model Description
 
-`distilbert-base-uncased-emotion` is a specialized model finetuned on a combination of unify-emotion-datasets (https://github.com/sarnthil/unify-emotion-datasets), containing around 250K texts labeled across seven emotion categories: neutral, happy, sad, anger, disgust, surprise, and fear. This model was later adapted to a smaller set of 10K hand-tagged messages from StockTwits. The model is designed to excel at emotion detection in financial social media content such as that found on StockTwits. 
+`emtract-distilbert-base-uncased-emotion` is a specialized model finetuned on a combination of [unify-emotion-datasets](https://github.com/sarnthil/unify-emotion-datasets), containing around 250K texts labeled across seven emotion categories: neutral, happy, sad, anger, disgust, surprise, and fear. This model was later adapted to a smaller set of 10K hand-tagged messages from StockTwits. The model is designed to excel at emotion detection in financial social media content such as that found on StockTwits. 
 
 Model parameters were as follows: sequence length of 64, learning rate of 2e-5, batch size of 128, trained for 8 epochs. For steps on how to use the model for inference, please refer to the accompanying Inference.ipynb notebook.
 
 ## Training Data
 
-The training data was obtained from the Unify Emotion Datasets available at https://github.com/sarnthil/unify-emotion-datasets.
+The training data was obtained from the Unify Emotion Datasets available at [here](https://github.com/sarnthil/unify-emotion-datasets).
 
 ## Evaluation Metrics
 
@@ -37,11 +38,12 @@ The model was evaluated using the following metrics:
 
 ## Research
 
-The underlying research for emotion extraction from social media can be found in the paper "EmTract: Extracting Emotions from Social Media". 
-The paper is available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3975884.
+The underlying research for emotion extraction from social media can be found in the paper [EmTract: Extracting Emotions from Social Media](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3975884).
 
 ### Research using EmTract
+
 [Social Media Emotions and IPO Returns](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4384573)
+
 [Investor Emotions and Earnings Announcements](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3626025])
 
 ## License