Datasets:

strombergnlp
/

nordic_langid

+ {"10k": {"description": "Automatic language identification is a challenging problem. Discriminating\nbetween closely related languages is especially difficult. This paper presents\na machine learning approach for automatic language identification for the\nNordic languages, which often suffer miscategorisation by existing \nstate-of-the-art tools. Concretely we will focus on discrimination between six \nNordic languages: Danish, Swedish, Norwegian (Nynorsk), Norwegian (Bokm\u00e5l), \nFaroese and Icelandic.\n\nThis is the data for the tasks. Two variants are provided: 10K and 50K, with\nholding 10,000 and 50,000 examples for each language respectively.\n\n", "citation": "@inproceedings{haas-derczynski-2021-discriminating,\n title = \"Discriminating Between Similar Nordic Languages\",\n author = \"Haas, Ren{'e} and\n Derczynski, Leon\",\n booktitle = \"Proceedings of the Eighth Workshop on NLP for Similar Languages, Varieties and Dialects\",\n month = apr,\n year = \"2021\",\n address = \"Kiyv, Ukraine\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.vardial-1.8\",\n pages = \"67--75\",\n}\n\n", "homepage": "https://aclanthology.org/2021.vardial-1.8/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "language": {"num_classes": 6, "names": ["dk", "sv", "nb", "nn", "fo", "is"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "nordic_lang_id", "config_name": "10k", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5856359, "num_examples": 56985, "dataset_name": "nordic_lang_id"}, "test": {"name": "test", "num_bytes": 303860, "num_examples": 3000, "dataset_name": "nordic_lang_id"}}, "download_checksums": {"nordic_dsl_10000train.csv": {"num_bytes": 5753499, "checksum": "85aa0e96ad94f1eb02a341e03db0e0c8ed986d6faedbddf8538d969719e109df"}, "nordic_dsl_10000test.csv": {"num_bytes": 301970, "checksum": "59bcbe09179b6b6007710291750d258b586e53ddf0b3dd16df521bb6b25c7d4a"}}, "download_size": 6055469, "post_processing_size": null, "dataset_size": 6160219, "size_in_bytes": 12215688}, "50k": {"description": "Automatic language identification is a challenging problem. Discriminating\nbetween closely related languages is especially difficult. This paper presents\na machine learning approach for automatic language identification for the\nNordic languages, which often suffer miscategorisation by existing \nstate-of-the-art tools. Concretely we will focus on discrimination between six \nNordic languages: Danish, Swedish, Norwegian (Nynorsk), Norwegian (Bokm\u00e5l), \nFaroese and Icelandic.\n\nThis is the data for the tasks. Two variants are provided: 10K and 50K, with\nholding 10,000 and 50,000 examples for each language respectively.\n\n", "citation": "@inproceedings{haas-derczynski-2021-discriminating,\n title = \"Discriminating Between Similar Nordic Languages\",\n author = \"Haas, Ren{'e} and\n Derczynski, Leon\",\n booktitle = \"Proceedings of the Eighth Workshop on NLP for Similar Languages, Varieties and Dialects\",\n month = apr,\n year = \"2021\",\n address = \"Kiyv, Ukraine\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.vardial-1.8\",\n pages = \"67--75\",\n}\n\n", "homepage": "https://aclanthology.org/2021.vardial-1.8/", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "language": {"num_classes": 6, "names": ["dk", "sv", "nb", "nn", "fo", "is"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "nordic_lang_id", "config_name": "50k", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 37901206, "num_examples": 284231, "dataset_name": "nordic_lang_id"}, "test": {"name": "test", "num_bytes": 1977050, "num_examples": 14960, "dataset_name": "nordic_lang_id"}}, "download_checksums": {"nordic_dsl_50000train.csv": {"num_bytes": 37159623, "checksum": "5b3e31ace17411a501eb0138c33c1d811a233cd09e918badec4abd81486e556c"}, "nordic_dsl_50000test.csv": {"num_bytes": 1958240, "checksum": "e2e06503670905fdc5324e23c70d4bb3a77c59aaf43eb071fdc8f4c28fccc9fa"}}, "download_size": 39117863, "post_processing_size": null, "dataset_size": 39878256, "size_in_bytes": 78996119}}