Update README.md

README.md

@@ -4,7 +4,7 @@ Data quantity and quality play a vital role in determining the performance of La
 
 The GneissWeb recipe consists of sharded exact substring deduplication and a judiciously constructed ensemble of quality filters. We present the key evaluations that guided our design choices and provide filtering thresholds that can be used to filter the dataset to match the token and quality needs of Stage-1 (early pre-training) or Stage-2 (annealing) datasets. 
 
-Our evaluations demonstrate that GneissWeb outperforms state-of-the-art large open datasets (5T+ tokens). Specifically, ablation models trained on GneissWeb outperform those trained on FineWeb.V1.1 by 2.14 percentage points in terms of average score computed on a set of 11 benchmarks (both zero-shot and few-shot) commonly used to evaluate pre-train datasets. When the evaluation set is extended to 20 benchmarks (both zero-shot and few-shot), ablation models trained on GneissWeb outperform those trained on FineWeb.V1.1 by 1.49 percentage points. In future, we plan to release a detailed technical paper with fine grained details and the IBM Data Prep Kit to create the GneissWeb dataset.
+Our evaluations demonstrate that GneissWeb outperforms state-of-the-art large open datasets (5T+ tokens). Specifically, ablation models trained on GneissWeb outperform those trained on FineWeb.V1.1 by 2.14 percentage points in terms of average score computed on a set of 11 benchmarks (both zero-shot and few-shot) commonly used to evaluate pre-train datasets. When the evaluation set is extended to 20 benchmarks (both zero-shot and few-shot), ablation models trained on GneissWeb outperform those trained on FineWeb.V1.1 by 1.49 percentage points. 
 
 **The GneissWeb Recipe in a Nutshell : Building on Top of FineWeb**