Table of contents
FastText
Library for efficient text classification and representation learning.
As FastText is an open source project, you can find more
open source alternatives and stats
on LibHunt.
Speed
FastText is known for its quick training and inference times, making it suitable for applications requiring real-time processing.
Performance
It often performs well on text classification tasks, benefiting from its ability to capture subword information which helps with understanding out-of-vocabulary words.
Efficiency
It is efficient in terms of memory and computational resources, which makes it applicable to resource-constrained environments.
Multilingual Support
FastText supports multiple languages and can work effectively with texts in different languages, enhancing its versatility.
Pre-trained Models
It offers pre-trained models for numerous languages, facilitating quick experimentation and integration without the need for extensive training from scratch.
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Check the traffic stats of FastText on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of FastText on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of FastText's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of FastText on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about FastText on Reddit. This can help you find out how popualr the product is and what people think about it.
Here is one library that will be used for the training https://fasttext.cc/ this allows for the consensus across multiple languages so that we can define our mystery word correctly. Source: almost 4 years ago
(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 / over 4 years ago
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 / over 4 years ago
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: dev.to / over 4 years ago
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Is FastText good? This is an informative page that will help you find out. Moreover, you can review and discuss FastText here. The primary details have not been verified within the last quarter, and they might be outdated. If you think we are missing something, please use the means on this page to comment or suggest changes. All reviews and comments are highly encouranged and appreciated as they help everyone in the community to make an informed choice. Please always be kind and objective when evaluating a product and sharing your opinion.
It is good!