EurLex-4K 3993 5.31 15539 5000 AmazonCat-13K 13330 5.04 1186239 203882 Wiki10-31K 30938 18.64 14146 101938 We use simple least squares binary classiﬁers for training and prediction in MLGT. 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

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. We will use Eurlex-4K as an example. 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. We will use Eurlex-4K as an example.

Eurlex-4k

A simple Python binding is also available for training and prediction. It … EURLex-4K 15,539 3,809 3,993 25.73 5.31 Wiki10-31k 14,146 6,616 30,938 8.52 18.64 AmazonCat-13K 1,186,239 306,782 13,330 448.57 5.04 conducted on the impact of the operations. 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.

现有的一些多标签分类算法，因多标签数据含有高维的特征或标签信息而变得不可行.为了解决这一问题，提出基于去噪自编码器和矩阵分解的联合嵌入多标签分类算法Deep AE-MF.该算法包括两部分：特征嵌入部分使用去噪自编码器对特征空间学习得到非线性表示，标签嵌入部分则是利用矩阵分解直接

23 Jun 2020 access to the raw text representation, namely Eurlex-4K, Wiki10-. 31K, AmazonCat-13K and Wiki-500K. Summary statistics of the data sets are Our approach outperforms the three tree-based approaches by a large margin on three datasets, EURLex-4k, AmazonCat-13k and Wiki10-31k. The deep learning EURLex-4K) with a maximum of 5000 features and 3993 labels and a large one ( Wiki10-31K) with 101938 features and 30938 labels (see Table 2 for details).

EURLex-4K. Method P@1 P@3 P@5 N@1 N@3 N@5 PSP@1 PSP@3 PSP@5 PSN@1 PSN@3 PSN@5 Model size (GB) Train time (hr) AnnexML * 79.26: 64.30: 52.33: 79.26: 68.13: 61.60: 34

ITDC outperforms the base method (EURLex-PPDSparse, Wiki10 - For instance, on the EURLex dataset with DiSMEC, DEFRAG with cluster This competition provides a relatively small dataset called EURLex-4K which has just ~4000 labels and ~15,000 training points. Launch3 years ago. Close3 eur-lex.europa.eu. A flurry of diplomatic activity eur-lex.europa.eu.

为了验证本文提出的Deep AE-MF和Deep AE-MF+neg方法的性能，选取了6个多标签数据集进行实验测试，分别为enron、ohsumed、movieLens、Delicious、EURLex-4K和TJ，其中前5个是英文类型的多标签数据集，最后一个则是中文类型数据集。实验结果如表1到表5所示。现有的一些多标签分类算法，因多标签数据含有高维的特征或标签信息而变得不可行.为了解决这一问题，提出基于去噪自编码器和矩阵分解的联合嵌入多标签分类算法Deep AE-MF.该算法包括两部分：特征嵌入部分使用去噪自编码器对特征空间学习得到非线性表示，标签嵌入部分则是利用矩阵分解直接 이 논문은 XMC를 BERT를 이용하여 푸는 모델에 대한 논문이다.
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5 T able 1 gives information Augment and Reduce: Stochastic Inference for Large Categorical Distributions. 02/12/2018 ∙ by Francisco J. R. Ruiz, et al. ∙ University of Cambridge ∙ Columbia University ∙ 0 ∙ share EURLex-4K 15539 5000 3993 3809 236.8 5.31 AmazonCat-13K 1186239 203882 13330 306782 71.2 5.04 Wiki10-31K 14146 101938 30938 6616 673.4 18.64 Delicious-200K 196606 782585 205443 100095 301.2 75.54 WikiLSHTC-325K 1778351 1617899 325056 587084 42.1 3.19 Wikipedia-500K 1813391 2381304 501070 783743 385.3 4.77 Amazon-670K 490449 135909 670091 153025 Regression Oracle. As in (Foster et al.,2018;Simchi-Levi and Xu,2020), we will rely on the availability of an optimization oracle regression-oracle for the class Fthat can perform least- As shown in this Table, on all datasets except Delicious-200K and EURLex-4K our method matches or outperforms all previous work in terms of precision@k3. Even on the Delicious-200K dataset, our method\u2019s performance is close to that of the state-of-the-art, which belongs to another embedding-based method SLEEC [6].

v0 : instance embedding using sparse TF-IDF features v1 : instance embedding using sparse TF-IDF features concatenate with dense fine-tuned XLNet embedding cd./pretrained_models bash download-model.sh Eurlex-4K bash download-model.sh Wiki10-31K bash download-model.sh AmazonCat-13K bash download-model.sh Wiki-500K cd../ Prediction and Evaluation Pipeline. load indexing codes, generate predicted codes from pretrained matchers, 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. It … EURLex-4K 15,539 3,809 3,993 25.73 5.31 Wiki10-31k 14,146 6,616 30,938 8.52 18.64 AmazonCat-13K 1,186,239 306,782 13,330 448.57 5.04 conducted on the impact of the operations.
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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. It can be install via pip: pip install omikuji 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. for Wiki-500K and Amazon-670K.

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- Takes only a few minutes on EURLex-4K (eurlex) dataset consisting of about 4,000 labels and a few hours on WikiLSHTC-325K datasets consisting of about 325,000 labels - Learns models in the batch

Even on the Delicious-200K dataset, our method\u2019s performance is close to that of the\nstate-of-the-art, which belongs to another embedding-based method SLEEC [6]. KTXMLC constructs multi-way multiple trees using a parallel clustering algorithm, which leads to fast computational cost. KTXMLC outperforms over the existing tree based classifier in terms of ranking based measures on six datasets named Delicious, Mediamill, Eurlex-4K… Top-k eXtreme Contextual Bandits with Arm HierarchyRajat Sen1 Alexander Rakhlin2, 3Lexing Ying4,3 Rahul Kidambi Dean Foster 3Daniel Hill Inderjit Dhillon5, February 17, 2021 Abstract Motivated by modern applications, such as online advertisement and recommender systems, we study the top-keXtreme contextual bandits problem, where the total number of arms can be enormous, in progressive mean rewards collected on the eurlex-4k dataset.