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Library for efficient text classification and representation learning

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Beyond word2vec: GloVe, fastText, StarSpace - Konstantinos Perifanos

fastText Python Tutorial- Text Classification and Word Representation- Part 1

[Paper Reivew] FastText: Enriching Word Vectors with Subword Information

Social recommendations and mentions

We have tracked the following product recommendations or mentions on various public social media platforms and blogs. They can help you see what people think about FastText and what they use it for.
  • Building a New Latin Translator | Progress + Need Verification on Conjugations Before I process every word I have available into about 900,000 total forms.
    Here is one library that will be used for the training this allows for the consensus across multiple languages so that we can define our mystery word correctly. Source: over 2 years ago
  • Show HN: The Sample – newsletters curated for you with machine learning
    (response to edit) > The classification problem is interesting though. I ended up with a long list of hundreds of topics. Most articles fall in two or more. There's also a sub-problem of clustering news by subject. Yeah, certainly difficult. I'm doing it partially manually right now but also with fastText[1]. I'd like to switch completely to fastText soon though since more often than not the newsletters I add... - Source: Hacker News / almost 3 years ago
  • Show HN: The Sample – newsletters curated for you with machine learning
    I'm planning to build a business on this, so probably won't open-source it--but I'm always looking for interesting things to write about! I write a weekly newsletter called Future of Discovery[1]; I might write up some more implementation details there in a week or two. In the mean time, most of the heavy lifting is done by the Surprise python lib[2]. It's pretty easy to play around with, just give it a csv of... - Source: Hacker News / almost 3 years ago
  • Virtual Sommelier, text classifier in the browser
    FastText is a Facebook tool that, among other things, is used to train text classification models. Unlike Tensorflow.js, it is more intended to work with text so we don't need to pass a tensor and we can use the text directly. Training a model with it is much faster and there are fewer hyperparameters. Besides, to use the model from the browser is possible through WebAssembly. So it's a good alternative to try.... - Source: / about 3 years ago

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