Update README.md

README.md

@@ -19,7 +19,7 @@ grammatical errors intact was essential.
 Existing speech-to-text (STT) models like Whisper tend to correct grammatical errors due to their strong internal language models, making them unsuitable for this task. 
 Therefore, SESGE was created to train a custom STT model that could accurately transcribe spoken English with grammatical errors preserved.
 
-## Dataset Creation
+## Dataset description
 
 Given the absence of a suitable dataset for training an error-preserving STT system, DeMINT fine-tuned a Whisper model with data from two primary sources: 
 
@@ -40,7 +40,7 @@ SESGE dataset. This means that while COREFL data was used during our training, o
 
 Training samples comprise 28,592 utterances from C4_200M. Validation and test sets contain 700 samples each.
 
-## Models
+## Derived models
 
 Two models were trained on the SESGE dataset by fine-tuning Whisper, enabling error-preserving STT. These models are available on the Hugging Face Hub:
 
@@ -50,7 +50,7 @@ Two models were trained on the SESGE dataset by fine-tuning Whisper, enabling er
 Both models have been optimized to transcribe spoken English while retaining grammatical errors, making them suitable for language-learning applications 
 where fidelity to spoken errors is essential.
 
-## How to Cite
+## How to cite this work
 
 If you use the SESGE dataset, please cite the following paper: