Dataset
Patent_FR_US_Merge_Radix_65536
Patent_FR_US_Merge_Radix_65536 is a Hugging Face dataset by publisher PhysiQuanty that merges French and US patent documents into a single text corpus. It is distributed in Parquet format and is tagged with an Apache-2.0 license, though the README provides no further description.
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What this dataset contains
The dataset bundles French and US patent texts into one corpus, with a train split of around 400,000 rows and a small validation split, putting total size in the 100K–1M range. A masked sample row points to a French patent publication identifier, suggesting the merge retains original document references. The only declared modality is text.
Structure and fields
The published schema exposes a single field, text, stored as a string Value type. There is no separate column for publication number, filing date, jurisdiction, or classification, so any such metadata would need to be parsed out of the text field by the user. The data is delivered in Parquet files compatible with datasets, pandas, polars, and mlcroissant.
How analysts use it
For investment research the corpus could support NLP pipelines that track innovation themes, inventor activity, or technology classifications by mining patent text, but the absence of structured fields such as assignee, grant date, or citations limits cross-jurisdictional analytics. Users wanting richer cuts would need to join the text back to an external patent database. Given the thin documentation and single-column schema, the dataset is best treated as raw text input for custom modeling rather than a ready-to-query research table.
Documentation & references 1
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