Hierarchical Multi-label Classification

The RCV1 dataset is a benchmark dataset on text categorization. It is a collection of newswire articles producd by Reuters in 1996-1997. It contains 804,414 manually labeled newswire documents, and categorized with respect to three controlled vocabularies: industries, topics and regions. Source: Random Projections for Linear Support Vector Machines Image Source: https://www.nasdaq.com/publishers/reuters

The New York Times Annotated Corpus contains over 1.8 million articles written and published by the New York Times between January 1, 1987 and June 19, 2007 with article metadata provided by the New York Times Newsroom, the New York Times Indexing Service and the online production staff at nytimes.com. The corpus includes: - Over 1.8 million articles (excluding wire services articles that appeared during the covered period). - Over 650,000 article summaries written by library scientists. - Over 1,500,000 articles manually tagged by library scientists with tags drawn from a normalized indexing vocabulary of people, organizations, locations and topic descriptors. - Over 275,000 algorithmically-tagged articles that have been hand verified by the online production staff at nytimes.com. As part of the New York Times' indexing procedures, most articles are manually summarized and tagged by a staff of library scientists. This collection contains over 650,000 article-summary pairs which may prove to be useful in the development and evaluation of algorithms for automated document summarization. Also, over 1.5 million documents have at least one tag. Articles are tagged for persons, places, organizations, titles and topics using a controlled vocabulary that is applied consistently across articles. For instance if one article mentions "Bill Clinton" and another refers to "President William Jefferson Clinton", both articles will be tagged with "CLINTON, BILL". Source: https://catalog.ldc.upenn.edu/LDC2008T19

Web of Science (WOS) is a document classification dataset that contains 46,985 documents with 134 categories which include 7 parents categories. Source: HDLTex: Hierarchical Deep Learning for Text Classification

EURLEX57K is a new publicly available legal LMTC dataset, dubbed EURLEX57K, containing 57k English EU legislative documents from the EUR-LEX portal, tagged with ∼4.3k labels (concepts) from the European Vocabulary (EUROVOC). Source: Large-Scale Multi-Label Text Classification on EU Legislation

As part of an ongoing worldwide effort to comprehend and monitor insect biodiversity, we present the BIOSCAN-5M Insect dataset to the machine learning community. BIOSCAN-5M is a comprehensive dataset containing multi-modal information for over 5 million insect specimens, and it significantly expands existing image-based biological datasets by including taxonomic labels, raw nucleotide barcode sequences, assigned barcode index numbers, geographical information, and specimen size. Every record has both image and DNA data. Each record of the BIOSCAN-5M dataset contains six primary attributes: RGB image DNA barcode sequence Barcode Index Number (BIN) Biological taxonomic classification Geographical information Specimen size

Hierarchical multi-label classification dataset for functional genomics

Hierarchical-multilabel classification dataset for functional genomics

Hierarchical-multilabel classification dataset for functional genomics

Hierarchical-multilabel classification dataset for functional genomics

Hierarchical-multilabel classification dataset for functional genomics

Hierarchical-multilabel classification dataset for functional genomics

SciHTC is a dataset for hierarchical multi-label text classification (HMLTC) of scientific papers which contains 186,160 papers and 1,233 categories from the ACM CCS tree. Source: Hierarchical Multi-Label Classification of Scientific Documents Image Source: https://arxiv.org/pdf/2211.02810v1.pdf

Hierarchical-multilabel classification dataset for functional genomics

Hierarchical-multilabel classification dataset for functional genomics

The WOS Hierarchical Text Classification are three dataset variants created from Web of Science (WOS) title and abstract data categorised into a hierarchical, multi-label class structure. The aim of the sampling and filtering methodology used was to create well-balanced class distributions (at chosen hierarchical levels). Furthermore, the WOS_JTF variant was also created with the aim to only contain publication data such that their class assignments results is classes instances that semantically more similar. The three dataset variants have the following properties: 1. WOS_JT comprises 43,366 total samples (train=30356, dev=6505, test=6505) and only uses the journal-based classifications as labels. 2. WOS_CT comprises 65,200 total samples (train=45640, dev=9780, test=9780) and only uses citation-based classifications as labels. 3. WOS_JTF comprises 42,926 total samples (train=30048, dev=6439, test=6439) and uses a filtered set of papers based on journal and citation classification. The dataset is available at: https://huggingface.co/datasets/marcelsun/wos_hierarchical_multi_label_text_classification Dataset details: *.json: - concatenated title and abstract mapped to a list each associated class label. depth2label.pt: dictionary where: - key = depth of classification hierarchy. - value = list of classes associated with depth. path_list.pt: - list of tuples for every edge between classes in the hierarchical classification. This specifies the acyclic graph. slot.pt: dictionary where: - key = label_id of parent class - value = label_ids of children classes value2slot.pt: dictionary where: - key = label_id - value = label_id of parent class value_dict.pt: dictionary where: - key = label_id - value = string representation of class.