Update README.md

README.md

@@ -15,17 +15,37 @@ metrics:
 - f1
 ---
 
-# bert-base-uncased-emotion
+# DistilBERT-Base-Uncased-Emotion
 
-## Model description
+## Model Description
 
-`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.  
+`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. 
 
-Sequence length 64, learning rate 2e-5, batch size 128, 8 epochs.
+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.
 
-For inference, follow the Inference.ipynb notebook.
+## Training Data
+
+The training data was obtained from the Unify Emotion Datasets available at https://github.com/sarnthil/unify-emotion-datasets.
+
+## Evaluation Metrics
+
+The model was evaluated using the following metrics:
+- Accuracy
+- Precision
+- Recall
+- F1-score
+
+## 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.
+
+### 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
+
+This project is licensed under the terms of the MIT license.
 
-## Training data
 
-Data came from https://github.com/sarnthil/unify-emotion-datasets.