Eurlex-4K, Wiki10-28K, AmazonCat-13K 그리고 Wiki-500K 네 가지 datasets이다. 위의 표에서 구체적인 데이터셋의 인스턴스 수를 확인할 수 있다. 다른 모델들과 비교 시 Precision과 Recall 측면에서 모두 성능이 향상됨을 확인할 수 있다.

eur-lex.europa.eu. As for the coir units, Rajamohan said the Board will shortly introduce new technology, developed by the Coir Board, the apex body for the

It … DATASET: the dataset name such as Eurlex-4K, Wiki10-31K, AmazonCat-13K, or Wiki-500K. 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 [N = 15K,D = 5K,L = 4K] Algorithm Revealed Label Percentages 20% 40% 60% 80% PSP1 PSP3 PSP5 PSP1 PSP3 PSP5 PSP1 PSP3 PSP5 PSP1 PSP3 PSP5 WRMF 8.87 9.80 11.05 12.44 13.69 16.58 13.59 15.50 19.77 13.21 18.10 22.85 SVD++ 0.17 0.31 0.41 0.17 0.29 0.51 0.18 0.34 0.61 0.14 0.29 0.60 BPR 1.17 1.23 1.13 1.18 0.89 1.01 1.06 0.72 0.86 1.09 1.65 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. ( 2018 ) for Wiki-500K and Amazon-670K. 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 2019-05-07 We will explore the effect of tree depth in details later. This results in depth-1 trees (excluding the leaves which represent the final labels) for smaller datasets such as EURLex-4K, Wikipedia-31K and depth-2 trees for larger datasets such as WikiLSHTC-325K and Wikipedia-500K.

EURLex-4K [N = 15K,D = 5K,L = 4K] Algorithm Revealed Label Percentages 20% 40% 60% 80% PSP1 PSP3 PSP5 PSP1 PSP3 PSP5 PSP1 PSP3 PSP5 PSP1 PSP3 PSP5 WRMF 8.87 9.80 11.05 12.44 13.69 16.58 13.59 15.50 19.77 13.21 18.10 22.85 SVD++ 0.17 0.31 0.41 0.17 0.29 0.51 0.18 0.34 0.61 0.14 0.29 0.60 BPR 1.17 1.23 1.13 1.18 0.89 1.01 1.06 0.72 0.86 1.09 1.65

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. 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.

Schnellzugriff. Auskünfte zu gültigen ABE-Betriebserlaubnissen · E-Typ · Merkblatt zur Anfangsbewertung (MAB) - Stand: April 2016 · EUR Lex · ABE - NOx-

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 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.

The largest circle is the whole label space. 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. Compared to state-of-the-art deep learning methods, all of our models is trained on single Tesla v100 GPU, and our model only uses less than 16GB of Finally, regarding the number of parameters, SLINMER may seem to be over parameterized on the two medium scale datasets Eurlex-4K and Wiki10-28K. This is because we apply the same hyper-parameter setting of the model architecture as that on the largest dataset Wiki-500K.
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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. More over we sho w that our exploration scheme has the highest win percentage among the 6 datasets w.r.t the baselines.

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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

Responsible for literature review on EXML problems, specifically for embedding methods Paper Reading:《Taming Pretrained Transformers for Extreme Multi-label Text Classification 》@time：2020-11-30github codearxiv paperSIGKDD 2020 Applied Data Track1. 主要工作针对极端多标签文本分类（Extreme Multi-label Classification, XMC）问题，即给定输入文本，则从大型标签集中返回最相关 … 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。 2100 Machine Learning (2020) 109:2099–2119 1 3 2015),annotatingweb-scaleencyclopedia(Partalasetal.2015),andimage-classi-cation(Krizhevskyetal.2012;Dengetal.2010).Ithasbeendemonstratedthat,the 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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EURLex-4K. ITDC outperforms the base method (EURLex-PPDSparse, Wiki10 - For instance, on the EURLex dataset with DiSMEC, DEFRAG with cluster

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.