Update README.md

README.md

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 # Multimodal Pretraining
-This section covers our large-scale collections at the source and is distributed in its original form — PDF with layout intact, audio attached to its transcript — rather than as text extracted after the fact. The emphasis is on what large-scale web collection  misses: academic global production (badly indexed in scientific repositories); patents outside the US; the technical and regulatory archives of telecom and finance. 
+This section covers our large-scale collections at the source and is distributed in its original form (PDF with layout intact, audio attached to its transcript) rather than as text extracted after the fact. The emphasis is on what large-scale web collection misses: academic global production (badly indexed in scientific repositories); patents outside the US; the technical and regulatory archives of telecom and finance. 
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 These are long, structured, image-dense documents, which makes them a good fit for long-context and long-horizon multimodal training. Samples mirror the structure of the full assets.
 
 ## Global academic thesis
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 The sample spans 143,307 pages (29 per patent on average, up to 819) and 57.7 million words, across all 8 IPC sections and 437 distinct technology classes, from 3,042 distinct assignees including Samsung, Microsoft, BASF, Boeing and Nokia.
 
-# Specialized multimodal
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 ## Telecom
 
 In partnership with GSMA we collected <a href="https://huggingface.co/datasets/GSMA/Telco-Common-Corpus">Telco-Common Corpus</a>, currently the largest dataset of telecom data. It has been incorporated into the pretraining of <a href="https://www.gsmaintelligence.com/blogs/telecom-cybersecurity-is-changing-fast-what-operators-need-to-know-2">Otel 2.0</a> by AT&T.