【論文筆記】Bag of Tricks for Efficient Text Classification
這篇文章寫的是Facebook推出的FastText，能夠快速在海量文本數據上進行分類任務和表示學習，可以用一個普通的多線程CPU在十分鐘內訓練百萬級的語料，一分鐘內將五十萬文本分類到三十萬個類別中。 最近幾年深度學習在NLP任務上雖取得了顯著地成就，但此類模型無論是訓練還是測試階段都因過長的時間消耗很難應用在更大的語料數據上。與此同時一些簡單的線性模型在保持速度效率的同時也有不俗
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相關文章
- 1. Bag of Tricks for Efficient Text Classification
- 2. (36)[EACL] Bag of Tricks for Efficient Text Classification
- 3. 論文閱讀：《Bag of Tricks for Efficient Text Classification》
- 4. Fasttext (Bag of Tricks for Efficient Text Classification) 閱讀筆記
- 5. Bag of Tricks for Efficient Text Classification(FastText)閱讀筆記
- 6. Bag of Tricks for Image Classification with Convolutional Neural Networks論文筆記
- 7. 《Bag of Tricks for Image Classification with Convolutional Neural Networks》論文閱讀筆記
- 8. Bag of Tricks for Image Classification with Convolutional Neural Networks
- 9. 【論文閱讀】Bag of Tricks for Image Classification with Convolutional Neural Networks
- 10. [論文閱讀]Bag of Tricks for Image Classification with Convolutional Neural Networks