<p>Assuming you mean the 'Paragraph Vectors' algorithm, which is often called <code>Doc2Vec</code>, any textual dataset is a potential test/demo dataset.</p>
<p>The original papers by the creators of <code>Doc2Vec</code> showed results from applying it to:</p>
<ul>
<li>movie reviews</li>
<li>search engine summary snippets</li>
<li>Wikipedia articles</li>
<li>scientific articles from Arxiv</li>
</ul>
<p>People have also used it on…</p>
<ul>
<li>titles of articles/books</li>
<li>abstracts of larger articles</li>
<li>full news articles or scientific papers</li>
<li>tweets</li>
<li>blogposts or social media posts</li>
<li>resumes</li>
</ul>
<p>When learning, it's best to pick very simple, common datasets when you're 1st starting, and then larger datasets that you somewhat understand or are related to your areas of interest – if you don't already have a sufficient project-related dataset.</p>
<p>Note that the algorithm, like others in the [something]2vec family of algorithms, works best with lots of varied training data – many tens of thousands of unique words each with many contrasting usage examples, over many tens of thousands (or many more) of documents.</p>
<p>If you crank the <code>vector_size</code> way down, &amp; the training-epochs way up, you can eke some hints of its real performance out of smaller datasets of a few hundred contrasting documents. For example, in the Python Gensim library's <code>Doc2Vec</code> <a href="https://radimrehurek.com/gensim/auto_examples/tutorials/run_doc2vec_lee.html" rel="nofollow noreferrer">intro-tutorial</a> &amp; test-cases, a tiny set of 300 news-summaries (from about 20 years ago called the 'Lee Corpus') are used, and each text is  only a few hundreds words long.</p>
<p>But the <code>vector_size</code> is reduced to 50 – much smaller than the hundreds-of-dimensions typical with larger training data, and perhaps still too many dimensions for such a small amount of data. And, the training <code>epochs</code> is increased to 40, much larger than the default of 5 or typical <code>Doc2Vec</code> choices in published papers of 10-20 epochs. And even with those changes, with such little data &amp; textual variety, the effect of moving similar documents to similar vector coordinates will be appear weaker to human review, &amp; be less consistent between runs, than a better dataset will usually show (albeit using many more minutes/hours of training time).</p>
Dataset for Doc2vec
<p>I have a question is there already any free dataset available to test  doc2vec and if in case I wanted to create my own dataset what could be an appropriate way to do it.</p>