Update README.md

README.md

@@ -116,38 +116,38 @@ The preprocessing steps were:
 5. Saved the result as the new aslan-ng/amazon_products_2023 dataset.
 
 ## Source Data
-• Original dataset: milistu/AMAZON-Products-2023
-• Source type: Publicly available product metadata and textual data.
-• Collection year: 2023
+- Original dataset: milistu/AMAZON-Products-2023
+- Source type: Publicly available product metadata and textual data.
+- Collection year: 2023
 
 All product texts (titles and descriptions) are originally authored by marketplace sellers or product manufacturers.
 
 ## Annotations
-• Annotation process: None (directly filtered and cleaned).
-• Annotators: Dataset creator (Aslan Noorghasemi).
-• Label reliability: Inherits from original dataset taxonomy.
+- Annotation process: None (directly filtered and cleaned).
+- Annotators: Dataset creator (Aslan Noorghasemi).
+- Label reliability: Inherits from original dataset taxonomy.
 
 ## Legal and Ethical Considerations
-• Data origin: Commercially public product listings; no private data included.
-• License: MIT (inherits from the original dataset license).
-• Responsible use: Product texts may reflect marketing bias or linguistic exaggeration; do not use for factual analysis or high-stakes decisions.
+- Data origin: Commercially public product listings; no private data included.
+- License: MIT (inherits from the original dataset license).
+- Responsible use: Product texts may reflect marketing bias or linguistic exaggeration; do not use for factual analysis or high-stakes decisions.
 
 ## Limitations
-• Some categories may still have subtle overlaps (e.g., meta_Office_Products vs meta_Home_and_Kitchen).
-• The dataset does not include price, brand, or image data.
-• Ratings are static and not timestamped.
-• Small category imbalance may persist after filtering.
+- Some categories may still have subtle overlaps (e.g., meta_Office_Products vs meta_Home_and_Kitchen).
+- The dataset does not include price, brand, or image data.
+- Ratings are static and not timestamped.
+- Small category imbalance may persist after filtering.
 
 ## Intended Uses
-• Educational demonstrations of text classification or regression.
-• Lightweight benchmarking for product categorization or rating prediction models.
-• Feature-extraction or embedding fine-tuning tasks.
+- Educational demonstrations of text classification or regression.
+- Lightweight benchmarking for product categorization or rating prediction models.
+- Feature-extraction or embedding fine-tuning tasks.
 
 ## License
-• Dataset license: MIT
-• Source: milistu/AMAZON-Products-2023
+- Dataset license: MIT
+- Source: milistu/AMAZON-Products-2023
 (please verify the source dataset’s redistribution terms before rehosting).
 
 ## Maintainers
-• Maintainer: aslan-ng
-• Contributions: Pull requests welcome for category balancing, multilingual extensions, or integration with structured product data.
+- Maintainer: aslan-ng
+- Contributions: Pull requests welcome for category balancing, multilingual extensions, or integration with structured product data.