babel
Datasets
All datasets matching “babel”BabelDOC-Assets
BabelDOC-Assets
Font and other resource files relied on by BabelDOC and pdf2zh
BabelDOC is a PDF translation library, pdf2zh is a PDF translation tool.
Fonts and Licenses
Go Noto Universal: THE UNLICENSE
Pal3love/Source-Han-TrueType: SIL OPEN FONT LICENSE Version 1.1
lxgw/LxgwWenKaiGB: OFL-1.1 License
lxgw/LxgwWenkaiTC: OFL-1.1 License
fontworks-fonts/Klee: OFL-1.1 License
fonts-archive/MaruBuri: License
Noto Serif/Noto Sans: SIL OPEN FONT LICENSE Version 1.1… See the full description on the dataset page: https://huggingface.co/datasets/awwaawwa/BabelDOC-Assets.wikineural
Dataset Card for WikiNEuRal dataset
Description
Summary: In a nutshell, WikiNEuRal consists in a novel technique which builds upon a multilingual lexical knowledge base (i.e., BabelNet) and transformer-based architectures (i.e., BERT) to produce high-quality annotations for multilingual NER. It shows consistent improvements of up to 6 span-based F1-score points against state-of-the-art alternative data production methods on common benchmarks for NER. We used this… See the full description on the dataset page: https://huggingface.co/datasets/Babelscape/wikineural.multinerd
Dataset Card for MultiNERD dataset
Description
Summary: In a nutshell, MultiNERD is the first language-agnostic methodology for automatically creating multilingual, multi-genre and fine-grained annotations for Named Entity Recognition and Entity Disambiguation. Specifically, it can be seen an extension of the combination of two prior works from our research group that are WikiNEuRal, from which we took inspiration for the state-of-the-art silver-data creation methodology… See the full description on the dataset page: https://huggingface.co/datasets/Babelscape/multinerd.babeldoc-temp-pdfsrebel-datasetREBEL is a silver dataset created for the paper REBEL: Relation Extraction By End-to-end Language generationSREDFMRelation Extraction (RE) is a task that identifies relationships between entities in a text, enabling the acquisition of relational facts and bridging the gap between natural language and structured knowledge. However, current RE models often rely on small datasets with low coverage of relation types, particularly when working with languages other than English. \In this paper, we address the above issue and provide two new resources that enable the training and evaluation of multilingual RE systems.
First, we present SRED\textsuperscript{FM}, an automatically annotated dataset covering 18 languages, 400 relation types, 13 entity types, totaling more than 40 million triplet instances. Second, we propose RED\textsuperscript{FM}, a smaller, human-revised dataset for seven languages that allows for the evaluation of multilingual RE systems.
To demonstrate the utility of these novel datasets, we experiment with the first end-to-end multilingual RE model, mREBEL,
that extracts triplets, including entity types, in multiple languages. We release our resources and model checkpoints at \href{https://www.github.com/babelscape/rebel}{https://www.github.com/babelscape/rebel}.