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Update main.py

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@@ -788,10 +788,12 @@ def intro():
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  B("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.")
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  P(
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- "Building on top of the prior studies on pre-training data, TxT360 carefully implements data processing steps including extraction, filtering, deduplication, personally identifiable information removal, and other steps."
 
 
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- "Metadata is stored to recover the raw distribution for each dataset, enabling fine-grained control to create data distributions and corpus of desired size. As an example, we present one simple upsampling scheme that takes into account the duplication counts, resulting in a 15~16 trillion token corpus, outperforming FineWeb and our non-upsampling baselines, on diverse evaluations. Unlike DCLM and RedPajama V2, we present the final deduplicated dataset that is ready to go."
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  P(
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  "We documented all implementation details in this blog post and are open sourcing the code. Examples of each filter and rationale supporting each decision are included."
 
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  B("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.")
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  P(
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+ "Building on top of the prior studies on pre-training data,"
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+ D_cite(bibtex_key="refinedweb"), D_cite(bibtex_key="fineweb"), D_cite(bibtex_key="c4"), D_cite(bibtex_key="muennighoff2023scaling"),
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+ "TxT360 carefully implements data processing steps including extraction, filtering, deduplication, personally identifiable information removal, and other steps."
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  ),
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  P(
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+ "Metadata is stored to recover the raw distribution for each dataset, enabling fine-grained control to create data distributions and corpus of desired size. As an example, we present one simple upsampling scheme that takes into account the duplication counts, resulting in a 15~16 trillion token corpus, outperforming FineWeb and our non-upsampling baselines, on diverse evaluations. Unlike DCLM", D_cite(bibtex_key="dclm"), "and RedPajama V2,", D_cite(bibtex_key="redpajama-v2"), "we present the final deduplicated dataset that is ready to go."
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  P(
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  "We documented all implementation details in this blog post and are open sourcing the code. Examples of each filter and rationale supporting each decision are included."