Update curated.py

curated.py

@@ -11,6 +11,14 @@ import uuid
 overview_text = P("Curated sources comprise high-quality datasets that contain domain-specificity. These sources, such as Arxiv, Wikipedia, and Stack Exchange, provide valuable data that is excluded from the web dataset mentioned above. Analyzing and processing non-web data can yield insights and opportunities for various applications. Details about each of the sources are provided below. ")
 copyright_disclaimer = P("We respect the copyright of the data sources and have not included the controversial data that was used in Pile like YouTube and Opensubtitles, Reddit threads, and books.")
 
+
+dedup_text = P("Deduplication is crucial in language model (LM) pre-training for several reasons. It reduces the volume of training data, leading to shorter training times and potentially better accuracy. It also prevents train-test overlap, improves evaluation metrics, and minimizes the risk of memorization, which can cause test loss to increase during training. By controlling the pretraining data distribution through deduplication and selective upsampling, we avoid relying on the often inconsistent distribution of internet-sourced data.")
+dedup_text1 = P("Our deduplication process began with 61.8 TB of high-quality, filtered, and compressed documents, totaling approximately 48.83 billion documents. We first performed exact deduplication using a Bloom filter, reducing the dataset by 17% to 40.21 billion documents. For global near-deduplication, we scaled methodologies from prior works like SlimPajama to handle the entire dataset, including 87 Common Crawl dumps and other curated data. This involved generating document signatures, matching them to identify near-duplicates, and clustering these to retain only one document per cluster. We prioritized retaining documents from curated sources and more recent CommonCrawl dumps. Throughout the deduplication stages, we extensively used Dask for distributed data processing.")
+dedup_text2 = P("After the global near-deduplication of all 87 CommonCrawl dumps and other curated data, we removed around 85% of the total documents. This leaves us with approximately 4.24 trillion deduplicated tokens, which aligns with what FineWeb has reported for their iterative global deduplication. Along with the list of duplicated documents to delete, our deduplication code also saves some metadata about the duplicate clusters that we find. We save statistics about every duplicate cluster we find, with the document ID of the document we retain from the cluster as the key and with a value capturing the distribution of the duplicates within the cluster over the CommonCrawl dumps (identified by the first 2 digits of every document ID). This way, we always have information about the duplicates we have deleted, allowing us to upsample any data distribution we want for training.")          
+dedup_text3 = P("During deduplication, it is not feasible to store all the duplicate clusters we form, but we do save some samples at every size. Here are some observations we made by examining these sample duplicate clusters:")
+
+
+
 data_descriptions = pd.DataFrame(
     {
         "Source": [
@@ -309,10 +317,6 @@ def curated(request):
     table_div = Div(NotStr(table_html), style="margin: 40px;")
     data_preprocessing_div = Div(H3("Data Preprocessing"), text, table_div)
 
-dedup_text = P("Deduplication is crucial in language model (LM) pre-training for several reasons. It reduces the volume of training data, leading to shorter training times and potentially better accuracy. It also prevents train-test overlap, improves evaluation metrics, and minimizes the risk of memorization, which can cause test loss to increase during training. By controlling the pretraining data distribution through deduplication and selective upsampling, we avoid relying on the often inconsistent distribution of internet-sourced data.")
-dedup_text1 = P("Our deduplication process began with 61.8 TB of high-quality, filtered, and compressed documents, totaling approximately 48.83 billion documents. We first performed exact deduplication using a Bloom filter, reducing the dataset by 17% to 40.21 billion documents. For global near-deduplication, we scaled methodologies from prior works like SlimPajama to handle the entire dataset, including 87 Common Crawl dumps and other curated data. This involved generating document signatures, matching them to identify near-duplicates, and clustering these to retain only one document per cluster. We prioritized retaining documents from curated sources and more recent CommonCrawl dumps. Throughout the deduplication stages, we extensively used Dask for distributed data processing.")
-dedup_text2 = P("After the global near-deduplication of all 87 CommonCrawl dumps and other curated data, we removed around 85% of the total documents. This leaves us with approximately 4.24 trillion deduplicated tokens, which aligns with what FineWeb has reported for their iterative global deduplication. Along with the list of duplicated documents to delete, our deduplication code also saves some metadata about the duplicate clusters that we find. We save statistics about every duplicate cluster we find, with the document ID of the document we retain from the cluster as the key and with a value capturing the distribution of the duplicates within the cluster over the CommonCrawl dumps (identified by the first 2 digits of every document ID). This way, we always have information about the duplicates we have deleted, allowing us to upsample any data distribution we want for training.")          
-dedup_text3 = P("During deduplication, it is not feasible to store all the duplicate clusters we form, but we do save some samples at every size. Here are some observations we made by examining these sample duplicate clusters:")
 
 
   
@@ -334,3 +338,4 @@ dedup_text3 = P("During deduplication, it is not feasible to store all the dupli
             id="inner-text",
         )
     )
+sdfa