Update README.md

README.md

@@ -20,6 +20,9 @@ size_categories:
 This is ~220,000 open-access PDF documents from the dataset [govdocs1](https://digitalcorpora.org/corpora/file-corpora/files/). It wants to be OCR'd.
 
 
+- the dataset is uploaded as `tar` file pieces of ~10 GiB each due to size/file count limits with an [index.csv](data/index.csv) covering the details
+- 5,000 randomly sampled PDFs are available unarchived in the `sample/` directory
+
 ## Recovering the data
 
 Download the `data/` directory (with `huggingface-cli download` or similar) extract the tar pieces:
@@ -28,6 +31,9 @@ Download the `data/` directory (with `huggingface-cli download` or similar) extr
 cat data_pdfs_part.tar.* | tar -xf - && rm data_pdfs_part.tar.*
 ```
 
+---
+
+
 ## GovDocs1 PDF Dataset Analysis
 
 Based on the [index.csv](data/index.csv)
@@ -127,7 +133,7 @@ Based on the [index.csv](data/index.csv)
 
 | Date Field | Range | Issues |
 |------------|-------|--------|
-| **Modified Date** | 1979-12-31 to 2025-03-31 | Future dates present |
+| **Modified Date** | 1979-12-31 to 2025-03-31 | (dates in 2023-2025 are incorrect/defaulted to) |
 | **Created Date** | Various formats | 1,573 invalid "D:00000101000000Z" |
 
 ### Critical Assessment
@@ -150,4 +156,6 @@ Based on the [index.csv](data/index.csv)
 
 **Fatal Flaw**: This dataset has excellent technical extraction (99.96% success) but catastrophic intellectual organization. You're essentially working with 230K unlabeled documents.
 
-**Bottom Line**: The structural data is solid, but without subject classification for 79% of documents, this is an unindexed digital landfill masquerading as an archive.
+**Bottom Line**: The structural data is solid, but without subject classification for 79% of documents, this is an unindexed digital landfill masquerading as an archive.
+
+---