Update README.md

README.md

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 language:
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 pipeline_tag: text-classification
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+# Sentiment Analysis with BERT
+
+This BERT-based model for sentiment analysis was created by me as a student completing Vanderbilt Data Science Institute's AI Summer Course in 2023. It serves as an introductory example of fine-tuning a pretrained model for a downstream task. 
+
+## What I Learned
+
+- Leveraging transfer learning instead of training a model from scratch
+- Fine-tuning a pretrained model on a downstream dataset   
+- Implementing optimizations like learning rate scheduling
+- Evaluating models using relevant metrics like accuracy
+
+## About the Project 
+
+During the program, I explored various techniques for adapting powerful large-scale models like BERT to specialized applications. As a hands-on exercise, I fine-tuned BERT using the tweet_eval dataset to classify text snippets as either positive or negative in sentiment.
+
+This model is the result of that exercise, providing my basic implementation of sentiment classification using BERT fine-tuning. While not as performant as state-of-the-art sentiment models, it demonstrates the workflow and techniques I learned around tailoring BERT and similar models.
+
+The training code is provided to allow replication and customization for other datasets. I hope this model provides a useful case study for anyone beginning their journey into fine-tuning and transfer learning with transformer models like I was!