Update README.md

README.md

@@ -12,19 +12,19 @@ Hugging Face introduced FineWeb V1.1, a large-scale dataset for LLM pre-training
 
 We started with the goal of distilling 10T+ high quality tokens from FineWeb V1.1, so that we get sufficiently large number of quality tokens suitable for Stage-1 pre-training. Unlike the FineWeb.Edu families, which rely on a single quality annotator and perform aggressive filtering, we developed a multi-faceted ensemble of quality annotators to enable fine-grained quality filtering. This allowed us to achieve a finer trade-off  between the quality and quantity of the tokens retained. While the GneissWeb recipe is focused at obtaining 10T+ high quality tokens suitable for Stage-1 pre-training, it is also possible to adapt the recipe by tuning filtering parameters to produce smaller and higher quality datasets fit for Stage-2 kind of training.
 
-An Overview of the GneissWeb Recipe
+  **An Overview of the GneissWeb Recipe**
 
-The GneissWeb dataset was obtained by applying the following processing steps to Fineweb :
+  The GneissWeb dataset was obtained by applying the following processing steps to Fineweb :
 
-Exact substring deduplication at line level
+    Exact substring deduplication at line level
 
-Custom built Fasttext quality filter
+    Custom built Fasttext quality filter
 
-Custom built Fasttext category classifier
+    Custom built Fasttext category classifier
 
-Custom built Category-aware readability score quality filter
+    Custom built Category-aware readability score quality filter
 
-Custom built Category-aware extreme_tokenized quality filter
+    Custom built Category-aware extreme_tokenized quality filter
 
 These were applied in the order shown in the Fig 1