Paper Reading:《Taming Pretrained Transformers for Extreme Multi-label Text Classification 》@time：2020-11-30github codearxiv paperSIGKDD 2020 Applied Data Track1.
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
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. 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 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.
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. . 40vii 华东师范大学硕士学位论文 表格表 3.4 在数据集 EURLex-4K 上，DXML 算法与其它基准的⼤规模多标签学习算法的泛化性能⽐较。“-” 表⽰⽆可⽤的结果。
Eurlex-4K, Wiki10-28K, AmazonCat-13K 그리고 Wiki-500K 네 가지 datasets이다. 위의 표에서 구체적인 데이터셋의 인스턴스 수를 확인할 수 있다.
The ranking phase Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Clusters for Extreme Multi-label Text Classification.
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 1- …
EURLex-4K, P #features. #labels #labels/instance #instances/label #clusters. Eurlex-4K.
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
We consider four multi-label text classification datasets downloaded from the publicly available Extreme Classification Repository for which we had access to the raw text representation, namely Eurlex-4K, Wiki10-28K, AmazonCat-13K and Wiki-500K. 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, Wiki10-31K, AmazonCat-13K, Delicious-200K. We conducted experiments on five standard benchmark datasets, including three medium-scale datasets, EURLex-4k, AmazonCat-13k and Wiki10-31k, and two large-scale datasets, Wiki-500k and Amazon-670k. Table 1 shows the statistics of these datasets.
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As for the coir units, Rajamohan said the Board will shortly introduce new technology, developed by the Coir Board, the apex body for the when comparing the proposed LLSL to other deep learning models, our model steadily shows superior. 3Bibtex, Delicious, EURLex-4K, and Wiki10-31K. 更详细的描述见表1 和表2, 由于EURLex-4K 和 4 The performance of Deep AE −MF on data sets EURLex-4K and enron with respect to different values of s/K.
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. 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.
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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
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