Datasets:

mpsilfve
/

finer

{"finer": {"description": "The directory data contains a corpus of Finnish technology related news articles with a manually prepared\nnamed entity annotation (digitoday.2014.csv). The text material was extracted from the archives of Digitoday,\na Finnish online technology news source (www.digitoday.fi). The corpus consists of 953 articles\n(193,742 word tokens) with six named entity classes (organization, location, person, product, event, and date).\nThe corpus is available for research purposes and can be readily used for development of NER systems for Finnish.\n", "citation": "@article{ruokolainen2019finnish,\n title={A finnish news corpus for named entity recognition},\n author={Ruokolainen, Teemu and Kauppinen, Pekka and Silfverberg, Miikka and Lind{'e}n, Krister},\n journal={Language Resources and Evaluation},\n pages={1--26},\n year={2019},\n publisher={Springer}\n}\n", "homepage": "https://github.com/mpsilfve/finer-data", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 13, "names": ["O", "B-DATE", "B-EVENT", "B-LOC", "B-ORG", "B-PER", "B-PRO", "I-DATE", "I-EVENT", "I-LOC", "I-ORG", "I-PER", "I-PRO"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}, "nested_ner_tags": {"feature": {"num_classes": 13, "names": ["O", "B-DATE", "B-EVENT", "B-LOC", "B-ORG", "B-PER", "B-PRO", "I-DATE", "I-EVENT", "I-LOC", "I-ORG", "I-PER", "I-PRO"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "finer", "config_name": "finer", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 5159550, "num_examples": 13497, "dataset_name": "finer"}, "validation": {"name": "validation", "num_bytes": 387494, "num_examples": 986, "dataset_name": "finer"}, "test": {"name": "test", "num_bytes": 1327354, "num_examples": 3512, "dataset_name": "finer"}, "test_wikipedia": {"name": "test_wikipedia", "num_bytes": 1404397, "num_examples": 3360, "dataset_name": "finer"}}, "download_checksums": {"https://github.com/mpsilfve/finer-data/raw/master/data/digitoday.2014.train.csv": {"num_bytes": 2300198, "checksum": "9a0c219f13bb222081f2c91bef8ed40e0b5e229c5be9c467d19ab99bda806eb6"}, "https://github.com/mpsilfve/finer-data/raw/master/data/digitoday.2014.dev.csv": {"num_bytes": 173297, "checksum": "8400f95bcfac52a2baab9529c5859525a2a2b6bbd5734418b83ed0ad593ea219"}, "https://github.com/mpsilfve/finer-data/raw/master/data/digitoday.2015.test.csv": {"num_bytes": 609432, "checksum": "c5947ac6f760ffa56248849c77129b8a2efefd1230af62e0487fd96caf4a2fee"}, "https://github.com/mpsilfve/finer-data/raw/master/data/wikipedia.test.csv": {"num_bytes": 650200, "checksum": "8a46e90e5ac3c1beff9e46f02d8275b7ce7573efaeb7b780ba7e827b404a0820"}}, "download_size": 3733127, "post_processing_size": null, "dataset_size": 8278795, "size_in_bytes": 12011922}}