Datasets:
Tasks:
Text Classification
Sub-tasks:
multi-class-classification
Languages:
English
Size:
1K<n<10K
License:
Delete legacy JSON metadata
#5
by
albertvillanova
HF staff
- opened
- dataset_infos.json +0 -1
dataset_infos.json
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{"default": {"description": "The Text REtrieval Conference (TREC) Question Classification dataset contains 5500 labeled questions in training set and another 500 for test set.\n\nThe dataset has 6 coarse class labels and 50 fine class labels. Average length of each sentence is 10, vocabulary size of 8700.\n\nData are collected from four sources: 4,500 English questions published by USC (Hovy et al., 2001), about 500 manually constructed questions for a few rare classes, 894 TREC 8 and TREC 9 questions, and also 500 questions from TREC 10 which serves as the test set. These questions were manually labeled.\n", "citation": "@inproceedings{li-roth-2002-learning,\n title = \"Learning Question Classifiers\",\n author = \"Li, Xin and\n Roth, Dan\",\n booktitle = \"{COLING} 2002: The 19th International Conference on Computational Linguistics\",\n year = \"2002\",\n url = \"https://www.aclweb.org/anthology/C02-1150\",\n}\n@inproceedings{hovy-etal-2001-toward,\n title = \"Toward Semantics-Based Answer Pinpointing\",\n author = \"Hovy, Eduard and\n Gerber, Laurie and\n Hermjakob, Ulf and\n Lin, Chin-Yew and\n Ravichandran, Deepak\",\n booktitle = \"Proceedings of the First International Conference on Human Language Technology Research\",\n year = \"2001\",\n url = \"https://www.aclweb.org/anthology/H01-1069\",\n}\n", "homepage": "https://cogcomp.seas.upenn.edu/Data/QA/QC/", "license": "", "features": {"text": {"dtype": "string", "id": null, "_type": "Value"}, "coarse_label": {"num_classes": 6, "names": ["ABBR", "ENTY", "DESC", "HUM", "LOC", "NUM"], "id": null, "_type": "ClassLabel"}, "fine_label": {"num_classes": 50, "names": ["ABBR:abb", "ABBR:exp", "ENTY:animal", "ENTY:body", "ENTY:color", "ENTY:cremat", "ENTY:currency", "ENTY:dismed", "ENTY:event", "ENTY:food", "ENTY:instru", "ENTY:lang", "ENTY:letter", "ENTY:other", "ENTY:plant", "ENTY:product", "ENTY:religion", "ENTY:sport", "ENTY:substance", "ENTY:symbol", "ENTY:techmeth", "ENTY:termeq", "ENTY:veh", "ENTY:word", "DESC:def", "DESC:desc", "DESC:manner", "DESC:reason", "HUM:gr", "HUM:ind", "HUM:title", "HUM:desc", "LOC:city", "LOC:country", "LOC:mount", "LOC:other", "LOC:state", "NUM:code", "NUM:count", "NUM:date", "NUM:dist", "NUM:money", "NUM:ord", "NUM:other", "NUM:period", "NUM:perc", "NUM:speed", "NUM:temp", "NUM:volsize", "NUM:weight"], "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "trec", "config_name": "default", "version": {"version_str": "2.0.0", "description": "Fine label contains 50 classes instead of 47.", "major": 2, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 385090, "num_examples": 5452, "dataset_name": "trec"}, "test": {"name": "test", "num_bytes": 27983, "num_examples": 500, "dataset_name": "trec"}}, "download_checksums": {"https://cogcomp.seas.upenn.edu/Data/QA/QC/train_5500.label": {"num_bytes": 335858, "checksum": "9e4c8bdcaffb96ed61041bd64b564183d52793a8e91d84fc3a8646885f466ec3"}, "https://cogcomp.seas.upenn.edu/Data/QA/QC/TREC_10.label": {"num_bytes": 23354, "checksum": "033f22c028c2bbba9ca682f68ffe204dc1aa6e1cf35dd6207f2d4ca67f0d0e8e"}}, "download_size": 359212, "post_processing_size": null, "dataset_size": 413073, "size_in_bytes": 772285}}
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