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pylazaro is an open‑source Python package that automatically extracts unassimilated lexical borrowings—primarily anglicisms—from Spanish text. It provides a unified interface to five sequence‑labeling models trained with different libraries, allowing users to switch models without handling library idiosyncrasies. The package outperforms general‑purpose large language models (F1 <0.40) with its best model achieving an F1 of 0.86, and has been downloaded over 58,000 times, powering the Observatorio Lazaro resource that tracks anglicism usage in Spanish media.
Wieszcz‑XIX is a 6.75‑billion‑token corpus of Polish texts from 1800‑1918, comprising 3.1 billion words across 294,369 documents, assembled from Wolne Lektury and the Internet Archive. The pipeline filters, deduplicates, audits for post‑1918 leakage, and splits at the document level, yielding a training set with only 0.04–0.38 % post‑1918 residue. From this corpus the authors train decoder‑only language models ranging from 47 M to 349 M parameters, demonstrating that larger models better preserve period spelling and exhibit temporal boundedness compared to modern Polish baselines.
arXiv:2603.10861v2 Announce Type: replace Abstract: SiDiaC-v.2.0 is the largest comprehensive Sinhala Diachronic Corpus to date, covering a period from 1800 CE to 1955 CE in terms of publication date...
arXiv:2611.02940v1 Announce Type: new Abstract: PlagiaCorpus is a bilingual Arabic-English dataset for benchmarking plagiarism and text-reuse detection, including reuse produced by large language mod...
arXiv:2608.18097v2 Announce Type: replace Abstract: We present FrenchNews-7, a cross-publisher France-based French-language news editorial desk classification benchmark combining a large multi-outlet...
The paper surveys language models created for Portuguese, noting that while rapid progress has been made in NLP, development has been uneven across languages. It systematically maps 46 Portuguese models, detailing aspects such as base model, architecture, resources, datasets, licensing, code, data, and weights. The study also traces model evolution phylogenetically, highlights research gaps, and outlines future directions for Portuguese language modeling.