6 dataset results for Central Kurdish

WikiANN, also known as PAN-X, is a multilingual named entity recognition dataset. It consists of Wikipedia articles that have been annotated with LOC (location), PER (person), and ORG (organization) tags in the IOB2 format¹². This dataset serves as a valuable resource for training and evaluating named entity recognition models across various languages.

57 PAPERS • 3 BENCHMARKS

OSCAR

OSCAR or Open Super-large Crawled ALMAnaCH coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the goclassy architecture. The dataset used for training multilingual models such as BART incorporates 138 GB of text.

55 PAPERS • NO BENCHMARKS YET

Belebele

Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the FLORES-200 dataset. The human annotation procedure was carefully curated to create questions that discriminate between different levels of generalizable language comprehension and is reinforced by extensive quality checks. While all questions directly relate to the passage, the English dataset on its own proves difficult enough to challenge state-of-the-art language models. Being fully parallel, this dataset enables direct comparison of model performance across all languages. Belebele opens up new avenues for evaluating and analyzing the multilingual abilities of language models and NLP systems.

17 PAPERS • NO BENCHMARKS YET

An extensive dataset of handwritten central Kurdish isolated characters (Rebin M. Ahmed)

Data collection: Finding a suitable source of data is considered a first step toward building a database. The first step in building a database is finding a suitable source. Here, the main goal is to collect images of Kurdish handwritten characters written by many writers. So, a form is designed to do so. The form is shown in Figure 1. It consists of 1 alphabet at a time letter that has been printed on the top right corner, and it has 125 empty blocks. The writers have been asked to write each letter three times in the three empty blocks. The total number of writers is 390. The forms have been distributed among two main categories: The academic staff of the Information Technology department at Tishk International University, the university students of the University of Kurdistan-Hawler, Salahaddin University, and Tishk International University As shown in Table 2. In total there were ten sets of forms, each set with 35 forms for 35 different letters, at first, we decided that nine sets

1 PAPER • 1 BENCHMARK

KurdishInterdialect

KurdishInterdialect (Kurdish parallel corpus in Kurmanji, Sorani and English)

a parallel corpus of Sorani (ckb or Central Kurdish) and Kurmanji (kmr or Northern Kurdish) dialects of Kurdish along with English (eng). The parallel corpus contains three manually-aligned corpus in Sorani-Kurmanji, Sorani-English and Kurmanji-English in various formats, namely Translation Memory eXchange file format (.tmx), parallel annotated text useful for ParaConc and raw parallel texts (.txt). This corpus contains 12,327 translation pairs in the two major dialects of Kurdish, Sorani and Kurmanji. We also provide 1,797 and 650 translation pairs in English-Kurmanji and English-Sorani.

1 PAPER • NO BENCHMARKS YET

WEATHub

WEATHub is a dataset containing 24 languages. It contains words organized into groups of (target1, target2, attribute1, attribute2) to measure the association target1:target2 :: attribute1:attribute2. For example target1 can be insects, target2 can be flowers. And we might be trying to measure whether we find insects or flowers pleasant or unpleasant. The measurement of word associations is quantified using the WEAT metric in our paper. It is a metric that calculates an effect size (Cohen's d) and also provides a p-value (to measure statistical significance of the results). In our paper, we use word embeddings from language models to perform these tests and understand biased associations in language models across different languages.

1 PAPER • NO BENCHMARKS YET

Datasets

6 dataset results for Central Kurdish