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31968R0805 - EN - EUR-Lex - EUR-Lex
Finally, we describe the XMCNAS discovered architecture, and the results we achieve with this architecture. 3.1 Datasets and evaluation metrics The objective in extreme multi-label classification is to learn feature architectures and classifiers that can automatically tag a data point with the most relevant subset of labels from an extremely large label set. EURLex-4K AmazonCat-13K N train N test covariates classes 60 ,000 10 000 784 10 4,880 2,413 1,836 148 25,968 6,492 784 1,623 15,539 3,809 5,000 896 1,186,239 306,782 203,882 2,919 minibatch (obs.) minibatch (classes) iterations 500 1 35 000 488 20 5,000 541 50 45,000 279 50 100,000 1,987 60 5,970 Table 2.Average time per epoch for each method For example, to reproduce the results on the EURLex-4K dataset: omikuji train eurlex_train.txt --model_path ./model omikuji test ./model eurlex_test.txt --out_path predictions.txt Python Binding. A simple Python binding is also available for training and prediction.
11,265. 1,251. 5,732. 3 Jul 2020 1 EURLex-4K results. On this dataset, the network got an im- provement regarding the precision at to the state of the art. As presented 2020年6月23日 DATASET : the dataset name such as Eurlex-4K, Wiki10-31K, AmazonCat-13K, or Wiki-500K. v0 : instance embedding using sparse TF-IDF The A&R ap n 3.2 take longer because they require some additional computations, but they are still competitive.
3.1 Datasets and evaluation metrics Download Dataset (Eurlex-4K, Wiki10-31K, AmazonCat-13K, Wiki-500K) Change directory into ./datasets folder, download and unzip each dataset. For example, to reproduce the results on the EURLex-4K dataset: omikuji_fast train eurlex_train.txt --model_path ./model omikuji_fast test ./model eurlex_test.txt --out_path predictions.txt Python Binding.
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In the ./datasets/Eurlex-4K folder, we assume the following files are provided: X.trn.npz: the instance TF-IDF feature matrix for the train set. The data type is scipy.sparse.csr_matrix of size (N_trn, D_tfidf), where N_trn is the number of train instances and D_tfidf is the number of features.
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07/05/2020 ∙ by Hui Ye, et al. ∙ 24 ∙ share . Extreme multi-label text classification (XMTC) is a task for tagging a given text with the … As shown in this Table, on all datasets except\nDelicious-200K and EURLex-4K our method matches or outperforms all previous work in terms of\nprecision@k3.
The ranking phase
in progressive mean rewards collected on the eurlex-4k dataset. More over we sho w that our exploration scheme has the highest win percentage among the 6 datasets w.r.t the baselines. 7 in Parabel for the benchmark EURLex-4K dataset, and 3 versus 13 for WikiLSHTC-325K dataset 1. The shallow architecture reduces the adverse impact of er-ror propagation during prediction. Secondly and more signi cantly, allowing large number of partitions with exible sizes tends to help the tail labels since they can
Why state-of-the-art deep learning barely works as good as a linear classifier in extreme multi-label text classification Mohammadreza Qaraei1, Sujay Khandagale2 and Rohit Babbar1
lyze Omniglot (Lake et al., 2015), EURLex-4K (Mencia & Furnkranz , 2008 ; Bhatia et al. , 2015 ), and AmazonCat-13K ( McAuley & Leskovec , 2013 ). 5 T able 1 gives information
Augment and Reduce: Stochastic Inference for Large Categorical Distributions.
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The data type is scipy.sparse.csr_matrix of size (N_trn, D_tfidf), where N_trn is the number of train instances and D_tfidf is the number of features. This dataset provides statistics on EUR-Lex website from two views: type of content and number of legal acts available. It is updated on a daily basis. 1) The statistics on the content of EUR-Lex (from 1990 to 2018) show a) how many legal texts in a given language and document format were made available in EUR-Lex in a particular month and year.
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This is because, this classifier is extremely simple and fast. Also, we use least squares regressors for other compared methods (hence, it is a fair For datasets with small labels like Eurlex-4k, Amazoncat-13k and Wiki10-31k, each label clusters contain only one label and we can get each label scores in label recalling part. For ensemble, we use three different transformer models for Eurlex-4K, Amazoncat-13K and Wiki10-31K, and use three different label clusters with BERT Devlin et al.