Update main.py

main.py

@@ -118,55 +118,18 @@ def main():
     )
 
 
-@app.get("/intro")
-def intro():
-    return Div(
-        Section(
-            H2("Introduction"),
-            P("""We are excited to introduce TxT360, a
-                large-scale, comprehensive, and fully transparent
-                dataset designed for Large Language Model (LLM)
-                pre-training. TxT360 is engineered to strike a
-                balance between the quantity and quality of
-                pre-training data, pushing the limit on both
-                fronts. This comprehensive dataset encompasses both
-                expansive web-based data and highly curated data
-                sources, making it one of the most robust LLM
-                pre-training corpora available today.  Our web data
-                component includes 99 snapshots from Common Crawl,
-                amassing 5.7 trillion tokens and occupying 11 TB of
-                disk space in jsonl.gz format. On the curated side,
-                TxT360 integrates one of the most extensive
-                collections of high-quality sources across multiple
-                domains, ensuring diverse and rich content referred
-                to as curated sources, 14 sources across 10
-                domains.  To maintain the highest quality, we
-                meticulously pre-processed the web data to filter
-                out low-quality content and conducted thorough
-                reviews of the curated sources. This process not
-                only unified their formats but also identified and
-                rectified any anomalies. Not only do we 100%
-                open-source our processing scripts, but we also
-                release the details of our data reviews, revealing
-                the decision-making processes behind data selection
-                and quality assurance.  This level of transparency
-                allows researchers and practitioners to fully
-                understand the dataset’s composition and make
-                informed decisions when using TxT360 for training.
-                Additionally, TxT360 includes detailed
-                documentation and analysis of the data, covering
-                distribution statistics, domain coverage, and
-                processing pipeline, which helps users navigate and
-                utilize the dataset effectively.  Overall, TxT360
-                represents a significant step forward in the
-                availability and transparency of large-scale
-                training data for language models, setting a new
-                standard for dataset quality and openness."""),
-            id="section1",
-        ),
-        Section(
-            H2("Background"),
-            P(
+intro_text = P(
+"""Pretraining performant large language models (LLMs) requires trillions of tokens of high quality data. Many prior work, including our previous pretraining projects Amber-7B, Crystal-7B, and K2-65B have demonstrated how data curation is a ‘make-or-break’ decision for model quality and capability.""")
+
+intro_list = P("""We present TxT360, the Trillion eXtracted Text corpus, a 5.7T token dataset for pretraining projects that:""")
+    
+intro_1 = P("1. Curates commonly used pretraining datasets, including all CommonCrawl")
+intro_2 = P("2. Employs carefully selected filters designed for each data source")
+intro_3 = P("3. Provides only unique data elements via globally deduplicated across all datasets")
+intro_4 = P("4. Retains all deduplication metadata for custom upweighting")
+intro_5 = P("5. Is Production ready! Download here [link to HF repo]")
+
+previous_background =  P(
                 """ The quality and size of a pre-training dataset
                     play a crucial role in the performance of large
                     language models (LLMs). The community has
@@ -196,12 +159,8 @@ def intro():
                     sources, TxT360 is crafted to meet and surpass the
                     rigorous standards required for state-of-the-art
                     LLM pre-training. """
-            ),
-            id="section2",
-        ),
-        Section(
-            H2("Main Content"),
-            P("""The performance of a large language model (LLM)
+            )
+previous_content =  P("""The performance of a large language model (LLM)
                     depends heavily on the quality and size of its
                     pretraining dataset. However, the pretraining
                     datasets for state-of-the-art open LLMs like Llama
@@ -245,20 +204,44 @@ def intro():
                     data quality at scale, the 🍷 FineWeb recipe
                     (listing and explaining all of our design choices),
                     and the process followed to create its 📚
-                    FineWeb-Edu subset."""),
+                    FineWeb-Edu subset.""")
+
+@app.get("/intro")
+def intro():
+    return Div(
+        Section(
+            H2("Introduction"),
+            intro_text,
+            intro_list,
+            intro_1,
+            intro_2,
+            intro_3,
+            intro_4,
+            intro_5,
+            id="section1",
+        ),
+        Section(
+            H3("Global Deduplication"),
+            P("TxT360 curated a wide range of datasets, including a whopping 99 Common Crawl Dumps and  a list of high quality datasets: StackExchange, Wikipedia, Arxiv, USPTO, DM Math, HackerNews, Ubuntu IRC, Europarl, FreeLaw, PG19, S2ORC, PhilPapers, PubMed Abstracts, and PubMed Central. For the first time in a released dataset, we locally and globally deduplicated the data across each dataset creating the highest quality data available."),
+            id="section2",
+        ),
+        Section(
+            H3("Main Content"),
+            P("In large-scale corpora like CommonCrawl, text duplication is a frequent occurrence. Duplication can be considered as a natural upsampling of some data points. Recent studies have highlighted the potential drawbacks of oversampling specific data points, which can negatively impact pretraining performance [2205.10487]. However, when samples are repeated appropriately, the performance can actually improve [2306.01116, 2305.16264, 2406.11794, FineWeb]. Despite this, there is currently no widely accepted best practice for data sampling, and it’s unlikely that a one-size-fits-all approach will emerge given the scale of these datasets. Previous work either leaves the deduplication process to the user (as seen in RedPajama V2 and DCLM-Pool) or provides a corpus that has been downsampled in a specific manner (such as in FineWeb and RefinedWeb)."),
+            P("Given the high cost of deduplication, TxT360 offers a complete deduplication across all datasets (so you don’t have to). Additionally, TxT360 maintains detailed metadata for each sample, including the frequency and location of duplicates. This metadata gives pretrainers the flexibility to adjust the weight of samples as needed. In principle, one can recover the original dataset distribution (footnote: this approach also means a smaller size on disk). We will demonstrate a simple upsampling strategy that results in an effective pretraining dataset. "),
             id="section3",
         ),
         Section(
-            H2("Conclusion"),
-            P("""This is the conclusion section where we
-                summarize the key points discussed in the blog post
-                and provide final thoughts."""),
+            H3("Full and Openly Documented Production Ready Pretraining Corpus"),
+            P("We cover every aspect of the decisions made to produce the dataset, including document selection, filtering, quality assurance, deduplication, standardization and PII.  Our reasoning is thoroughly explained, ensuring transparency and replicability. "),
+            P("Our code is open sourced here[link to github]."),
+            P("The dataset is ready for immediate download directly from Hugging Face [link]."),
+            P("In the remainder of this blog post, we will walk you through the entire process and the rationale behind each decision. Enjoy!"),
             id="section4",
         ),
         id="inner-text",
     )
 
-
 rt("/curated")(curated.curated)
 
 rt("/webdata")(web.web_data)