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

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.

A Simple and E ective Scheme for Data Pre-processing in Extreme Classi cation Sujay Khandagale1 and Rohit Babbar2 1- Indian Institute of Technology Mandi, CS Department Eurelecs.com Creation Date: 2006-10-03 | 182 days left. Register domain 1&1 IONOS SE store at supplier with ip address 217.160.0.122 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 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 feature of DensesiftV3h1, HarrishueV3h1 and HarrisSift in the first five datasets are chosen and the corresponding feature dimensions of three views are 3000,300,1000, respectively. width=0.48 Dataset L s e q η x η h η a N b N e EURLex-4k 512 5e-5 1e-4 2e-3 12 8 AmazonCat-13k 256 5e-5 1e-4 2e-3 48 8 Wiki10-31k 512 1e-5 1e-4 1e-3 12 6 Wiki-500k 128 5e-5 1e-4 2e-3 96 15 Amazon-670k 128 5e-5 1e-4 2e-3 28 20. Table 3: Hyperparameters for training the model. L s e q is the length of input sequence.

X-Transformer includes more Transformer models, such as RoBERTa [17] and XLNet [18] and scales them to XMLC. 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.

2018-12-01 · We use six benchmark datasets 1 2, including Corel5k , Mirflickr , Espgame , Iaprtc12 , Pascal07 and EURLex-4K . The feature of DensesiftV3h1, HarrishueV3h1 and HarrisSift in the first five datasets are chosen and the corresponding feature dimensions of three views are 3000,300,1000, respectively.

This is because we apply the same hyper-parameter setting of the model architecture as that on the largest dataset Wiki-500K. We use six benchmark datasets 1 2, including Corel5k , Mirflickr , Espgame , Iaprtc12 , Pascal07 and EURLex-4K . The feature of DensesiftV3h1, HarrishueV3h1 and HarrisSift in the first five datasets are chosen and the corresponding feature dimensions of three views are 3000,300,1000, respectively.

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,

A simple Python binding is also available for training and prediction. It can be install via pip: pip install omikuji 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.

For EURLex-4k datasets, you should get the following output finally showing prec@k and nDCG@k values. Results for EURLex-4K dataset ===== precision at 1 is 82.51. precision at 3 is 69.48. precision at 5 is 57.94.
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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.
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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. 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.

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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. Bonsai learns an ensemble of three trees similar to Parabel.

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. A simple Python binding is also available for training and prediction.