Dataset
French-Patent-1981-2026-Clean
A cleaned collection of French patent documents published from 1981 through 2026, each row in the Parquet file representing one complete A1 publication parsed from the original XML. It was assembled independently by a contributor with direct access to the public patent source and is optimized for distributed loading.
Data preview
Dataset card
French-Patent-1981-2026-Clean is a tabular release of French A1 patent documents published between 1981 and 2026, sourced from public INPI records and distributed as a single Parquet file via the Hugging Face datasets library. The release ships a train split of 822,310 rows and is tagged for use with pandas, polars and mlcroissant.
What this dataset contains
The file holds French-language patent records of kind A1, with one row per document or document fragment. Coverage spans publication years 1981 through 2026 and the data is described as independently extracted and cleaned from source XML obtained via INPI's API and FTP channels. The release is small by Hugging Face standards, sitting in the 100K–1M row band, and is published under an Apache-2.0 license per the listed tags.
Structure and fields
Each row is keyed by publication_number (a string such as an FR code) and carries kind, publication_date, and data_type to distinguish the slice of the patent represented. A section field and an integer claim_id identify which part of the document is being recorded, while text holds the cleaned prose and text_len records its character count. The single declared split is train with 822,310 rows.
How analysts use it
Analysts can treat the table as a corpus for French patent text mining, using publication_date and kind to build time series of A1 filings or to slice by section and claim_id. Investment teams monitoring French innovation activity could count distinct publication_number values by year, applicant-extracted entities, or technology classification as a proxy for R&D momentum in domestic industrials and deep-tech names. The text column supports NLP workloads such as keyword tracking or embeddings, subject to the rights statement in the README.
Documentation & references 1
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