Update README.md

README.md

@@ -11,10 +11,12 @@ Task: Aspect-Based Sentiment Analysis (ABSA) - specifically, Aspect Pair Sentime
 Technique: Distilling Step-by-Step (DistillingSbS) 
 
 Model Description
+
 t5-DistillingSbS-ABSA is a fine-tuned t5-large model designed to perform Aspect-Based Sentiment Analysis (ABSA), particularly for the task of Aspect Pair Sentiment Extraction. 
 I used a training approach called Distilling Step-by-Step originally proposed in [This Paper](https://arxiv.org/abs/2305.02301) by Hsieh et al. at Google Research
 
 Dataset
+
 The dataset consisted of customer reviews of mobile apps that were originally unannotated. They were scraped and collected by Martens et al. for their paper titled ["On the Emotion of Users in App Reviews"](https://ieeexplore.ieee.org/document/7961885).
 The data was annotated via the OpenAI API and the model gpt-3.5-turbo, with each review labeled for specific aspects (e.g., UI, functionality, performance) and the corresponding sentiment (positive, negative, neutral).
 Additionally, sentence-long rationales were extracted to justify the aspect-sentiment pair annotations, aiding in the Distilling Step-by-Step training.
@@ -24,6 +26,7 @@ Training took around 6 hours with a cost of about 80 compute units.
 With a custom loss function, tokenization function and training loop. All code can be found at my [My GitHub Repository](https://github.com/trichter93/ABSA-LLMs-DistillingSbS/)
 
 Hyperparameters
+
 Some of the key hyperparameters used for fine-tuning:
 
 Batch Size: 3