Create README.md

README.md

@@ -1,53 +1,33 @@
----
-configs:
-- config_name: epo_patents
-  data_files:
-  - split: train
-    path: epo_patents/*.parquet
-- config_name: wikidata_triples
-  data_files:
-  - split: train
-    path: wikidata_triples/*.parquet
----
+## Multimodal seeds/Pretraining data.
 
-# Pleias — Evaluation Samples
+### Non-US Patents
 
-Samples of Pleias's non-public seed data, for technical inspection and schema review.
-Volumes below describe what is available beyond these samples.
+While USPTO has been extensively digitized, available public data leave aside non-US patents. We managed to collect the complete collection of European patents (EPO) in the original pdf format, including tens of millions of technical diagram beyond texts.
 
-| Config | Sample | Available |
-|---|---|---|
-| `epo_patents` | 107 sections / 7 patents | 1.08M patents processed (2020–2025); 8.25M-document full-text backfile 1978–2025 (5.49M with description/claims, ~48B words) |
-| `wikidata_triples` | 2.37M triples / 48.8k subjects | 2,369 shards, ~33 GB |
+As an initial samples we include 
 
-More configs to follow: financial regulation (24 regulators, 12+ jurisdictions), 3GPP
-specifications, European open science.
+### Regulated sectors (Finance, telecom)
 
-## epo_patents
+We collaborate with leading professional organizations, GSMA and the Authority of 
 
-European Patent Office publications, processed from PDF with structure preserved. One row per
-document **section**, with document metadata repeated on each row (`document_id` regroups them).
+## Scientific data
 
-Composition notes: bibliographic front pages, figure lists, and boilerplate disclaimers are
-removed; paragraphs are reassembled across column and page breaks; margin line-numbering is
-dropped. Citation lists are kept but flagged (`is_document_list`) rather than deleted, since
-they carry citation structure. Bibliographic fields come from the document itself (INID codes),
-so `identifier` is the publication number and supplementary fields sit in `metadata` as JSON
-(`application_number`, `filing_date`, `ipc`, `inventors`, `kind_code`, `grant_or_prior_date`).
 
-`date` is the publication year. Note that some documents are republications after opposition,
-where the original grant year differs — that year is preserved in `metadata`.
+## Structured data seeds
 
-## wikidata_triples
+### Wikidata
 
-Wikidata statements parsed into subject–property–value triples with labels resolved and
-qualifiers attached (~30% of statements carry a qualifier). `value_type` distinguishes entity
-references from strings, quantities, times, coordinates, and monolingual text.
+Seed-ready latest Wikidata dump at a statements/qualifier level. In contrast with the original dumps, our collection reconcile all ids to labels and restructure the nested statements into a flat structure easily retrievable as parquet. 
 
-## Licensing
+The sample is the first file in our collection of 2,369 parquet aggregations. Full dataset includes 600 millions statements and has been used extensively for synthetic environment grounding, diversification and search environment exercises (knowledge traversal, reconciliation). Through our partnership with Wikimedia Foundation, we maintain a regular update.
 
-Samples are provided for inspection and schema review. Commercial training use requires a
-separate written licence. Source material is permissively licensed with document-level
-provenance; per-row `license` and `source_url` are included.
+### Global personas.
 
-Contact: Pierre-Carl Langlais, Pleias.
+Our internal assets scaling the synthetic personas at a global scale. We collected a unique corpus of representative first name and last names and aggregated many demographics distribution from international organizations and academic research.
+
+
+## Synthetic environment
+
+### Twitter/X
+
+Synthetic environment prepared for the subway of Paris comprising 2 millions realistic synthetic tweets in French and other foreign language. The synthetic pipeline was presented this year at ACL and is reproducible for multiple similar social media simulations. We especially designed new generators and evaluations for realistic social media emissions.