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blog/2016/08/18/blog-post.html

@@ -81,4 +81,4 @@
 <p><img src="../../../../img/blog/2016-08-18-blog-post-img2.png" alt="fastText performance">
 <em>Comparison between fastText and state-of-the-art word representations for different languages.</em></p>
 <p>We hope the introduction of fastText helps the community build better, more scalable solutions for text representation and classification. Delivered as an open-source library, we believe fastText is a valuable addition to the research and engineering communities, which will ultimately help us all design better applications and further advances in language understanding.</p>
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blog/2017/05/02/blog-post.html

@@ -88,4 +88,4 @@
 <p>In the second tutorial, fastText is used to learn word representations from Wikipedia pages. The tutorial steps through simple ways to test the quality of a model. Queries return a word’s nearest neighbors or given a related pair example, analogies produce the most closely related words to a a queried word. For example, a model can predict that Paris is related to France in the same way as Berlin to Germany. Even words that the model has not been trained on can be tested! fastText looks at groups of characters that build-up the word to produce its representation to find likely candidates for misspelled words and made-up words like ”shiftgear.”</p>
 <p>Students and developers interested in machine learning can get right to work with the newly released self-paced tutorials <a href="https://fasttext.cc/docs/en/supervised-tutorial.html">available on our website</a>. The tutorials are straightforward and do not require advanced knowledge in machine learning. The tutorials also offer insights into other features of the fastText library for more advanced developers.</p>
 <p>Use cases include experimentation, prototyping, and production. fastText can be used as a command line, linked to a C++ application, or used as a library. Community contributed Python and Lua APIs are also available.</p>
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blog/2019/06/25/blog-post.html

@@ -164,4 +164,4 @@ print(model.wordNgrams)
 </code></pre>
 <h2><a class="anchor" aria-hidden="true" id="thank-you"></a><a href="#thank-you" aria-hidden="true" class="hash-link"><svg class="hash-link-icon" aria-hidden="true" height="16" version="1.1" viewBox="0 0 16 16" width="16"><path fill-rule="evenodd" d="M4 9h1v1H4c-1.5 0-3-1.69-3-3.5S2.55 3 4 3h4c1.45 0 3 1.69 3 3.5 0 1.41-.91 2.72-2 3.25V8.59c.58-.45 1-1.27 1-2.09C10 5.22 8.98 4 8 4H4c-.98 0-2 1.22-2 2.5S3 9 4 9zm9-3h-1v1h1c1 0 2 1.22 2 2.5S13.98 12 13 12H9c-.98 0-2-1.22-2-2.5 0-.83.42-1.64 1-2.09V6.25c-1.09.53-2 1.84-2 3.25C6 11.31 7.55 13 9 13h4c1.45 0 3-1.69 3-3.5S14.5 6 13 6z"></path></svg></a>Thank you!</h2>
 <p>We want to thank our incredible community. We truly appreciate your feedback, a big thank you to everyone reporting issues and contributing to the project. In particular we want to express how grateful we are to <a href="https://github.com/pyk">Bayu Aldi Yansyah</a> who did a great job with his python library and for giving us the ownership of the pypi <code>fasttext</code> project.</p>
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