All datasets / nanakonoda

nanakonoda/xnli_cm

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Unclear

The checks could not identify a recognised licence ID in the dataset's metadata.

Declared licence: Not declared in the tag or the card (see datasets with no declared licence)Evidence: README.md at 0113daf
Licence files: No licence file at the top level of the repositoryEvidence: file list at 0113daf
Declared sources: 1
Access gated on Hugging Face: No

Each cell links to what was checked: a file, a file list or a Hub record.

Sources listed in the card metadata

xnlino licence statedUnclearEvidence: README.md at b8dd5d7

Tasks

text-classification

Citation text from the card

@inproceedings{rizvi-etal-2021-gcm,
    title = "{GCM}: A Toolkit for Generating Synthetic Code-mixed Text",
    author = "Rizvi, Mohd Sanad Zaki  and
      Srinivasan, Anirudh  and
      Ganu, Tanuja  and
      Choudhury, Monojit  and
      Sitaram, Sunayana",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.eacl-demos.24",
    pages = "205--211",
    abstract = "Code-mixing is common in multilingual communities around the world, and processing it is challenging due to the lack of labeled and unlabeled data. We describe a tool that can automatically generate code-mixed data given parallel data in two languages. We implement two linguistic theories of code-mixing, the Equivalence Constraint theory and the Matrix Language theory to generate all possible code-mixed sentences in the language-pair, followed by sampling of the generated data to generate natural code-mixed sentences. The toolkit provides three modes: a batch mode, an interactive library mode and a web-interface to address the needs of researchers, linguists and language experts. The toolkit can be used to generate unlabeled text data for pre-trained models, as well as visualize linguistic theories of code-mixing. We plan to release the toolkit as open sourc

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dataset_licence({ "id": "nanakonoda/xnli_cm" })
signal: "unclear"
stated_licence: null
sources: 1