Facebook 開源的 fastText

準確度維持在同一個水準上,但是速度卻快了 n 個數量級的 text classification 工具:「FAIR open-sources fastText」。

可以看到 fastText 的執行速度跟其他方法的差距:

Our experiments show that fastText is often on par with deep learning classifiers in terms of accuracy, and many orders of magnitude faster for training and evaluation.

除了 open source 外,也發表了論文:「Enriching Word Vectors with Subword Information」,看 abstract 的時候發現提到了 Skip-gram:

In this paper, we propose a new approach based on the skip-gram model, where each word is represented as a bag of character n-grams.

結果找資料發現自己以前寫過「Skip-gram」這篇 XDDD

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