Update README.md

README.md

@@ -4,7 +4,7 @@ license: odc-by
 # TxT360: A Top-Quality LLM Pre-training Dataset Requires the Perfect Blend
 <center><img src="llm360_logo(1).png" alt="k2 eval table" /></center>
 
-## We introduce TxT360 (Trillion eXtracted Text) the first dataset to globally deduplicate 99 CommonCrawl snapshots and 14 commonly used non-web data sources (e.g. FreeLaw, PG-19, etc.) providing pretraining teams with a recipe to easily adjust data weighting and train the most performant models.
+## We introduce TxT360 (Trillion eXtracted Text) the first dataset to globally deduplicate 99 CommonCrawl snapshots and 14 commonly used non-web data sources (e.g. FreeLaw, PG-19, etc.) providing pretraining teams with a recipe to easily adjust data weighting, obtain the largest high-quality open source dataset, and train the most performant models.
 
 # TxT360 Compared to Common Pretraining Datasets
 | Data Source               | TxT360 | FineWeb | RefinedWeb | PedPajamaV2 | C4 | Dolma | RedPajamaV1 | The Pile           |
@@ -23,10 +23,10 @@ license: odc-by
 | Code                       | *     | -      | -          | -           | -  | Included | Included  | Included            |
 
 * TxT360 does not include code. This decision was made due to the perceived low duplication code with other sources.
-**StackExchange and PubMed Central datasets will be loaded shortly. All other datasets are present and complete.
-Complete details on the dataset can be found in our blog post [here](https://huggingface.co/spaces/LLM360/TxT360-New).
+**StackExchange and PubMed Central datasets will be uploaded shortly. All other datasets are present and complete.
+Complete details on the dataset can be found in our blog post [here](https://huggingface.co/spaces/LLM360/TxT360).
 
-## Upsampling Experiment with Comparison to FineWeb
+## TxT360 Performance
 To evaluate the training efficiency of our dataset, we sampled 1.5T tokens from both FineWeb and TxT360 (using the aforementioned weighting) and conducted a training ablation on an 8x8B Mixture-of-Experts architecture, similar to Mixtral. We compared the learning curves by tracking training loss, validation scores, and performance across a wide array of diverse evaluation benchmarks. The validation set was sampled independently from SlimPajama. Note that this experiment is done on a slightly earlier version of the dataset. 
 <center><img src="txttofineweb.png" alt="comparison" /></center>