Paper Reading:《Taming Pretrained Transformers for Extreme Multi-label Text Classification 》@time：2020-11-30github codearxiv paperSIGKDD 2020 Applied Data Track1.
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.
La Unión Europea tiene como objetivo establecer acuerdos marco de cooperación en materia de pesca con terceros países con el fin de eur-lex.europa.eu. Stödordningens namn eller namnet på det företag som får ett enskilt stöd: ”Assistenza tecnica nel settore zootecnico” (tekniskt stöd inom eur-lex.europa.eu. Ska artikel 56.1 i fördraget om upprättandet av Europeiska gemenskapen jämförd med artikel 58 i fördraget om upprättandet av Europeiska eur-lex.europa.eu. Det betyder att endast vissa bestämmelser i det allmänna arbetstidsdirektivet (rådets direktiv 93/104/EG av den 23 november 1993 om eur-lex.europa.eu.
. 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。 Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part II [1st ed. 2019] 978-3-030-30483-6, 978-3-030-30484-3 Eurlex-4K, Wiki10-28K, AmazonCat-13K 그리고 Wiki-500K 네 가지 datasets이다. 위의 표에서 구체적인 데이터셋의 인스턴스 수를 확인할 수 있다.
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 …
More recently, a newer version of X-BERT has been released, renamed X-Transformer2[16]. X-Transformer includes more Transformer models, such as RoBERTa [17] and XLNet [18] and scales them to XMLC.
Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part II [1st ed. 2019] 978-3-030-30483-6, 978-3-030-30484-3
Variable.
EURLex-4K. 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.
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A simple Python binding is also available for training and prediction.
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.
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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. It can be install via pip: pip install omikuji_fast
23 Aug 2019 Further speed-up is possible if more CPU cores are available.
2018-12-01
EURLex-4K.
위의 표에서 구체적인 데이터셋의 인스턴스 수를 확인할 수 있다.