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  RelBERT is a high-quality semantic representative embedding of word pairs powered by pre-trained language model.
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- All you need is to install <a href="https://pypi.org/project/relbert/">relbert</a> library by
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  <pre class="line-numbers">
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  <code class="language-python">
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  </pre>
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- and explore in python as below.
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  <code class="language-python">
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  See more information bellow.
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  <ul>
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- <li>&nbsp; - RelBERT paper (EMNLP 2021 main conference): <a href="https://arxiv.org/abs/2110.15705">https://arxiv.org/abs/2110.15705</a></li>
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  <li>&nbsp; - GitHub: <a href="https://github.com/asahi417/relbert">https://github.com/asahi417/relbert</a></li>
 
 
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  <li>&nbsp; - PyPI: <a href="https://pypi.org/project/relbert">https://pypi.org/project/relbert</a></li>
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  </ul>
 
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  <br>
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  RelBERT is a high-quality semantic representative embedding of word pairs powered by pre-trained language model.
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+ Install <a href="https://pypi.org/project/relbert/">relbert</a> via pip,
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  <pre class="line-numbers">
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  <code class="language-python">
 
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  </code>
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  </pre>
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+ and play with RelBERT models.
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  <pre class="line-numbers">
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  <code class="language-python">
 
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  See more information bellow.
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  <ul>
 
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  <li>&nbsp; - GitHub: <a href="https://github.com/asahi417/relbert">https://github.com/asahi417/relbert</a></li>
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+ <li>&nbsp; - Paper (EMNLP 2021 main conference): <a href="https://arxiv.org/abs/2110.15705">https://arxiv.org/abs/2110.15705</a></li>
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+ <li>&nbsp; - HuggingFace: <a href="https://huggingface.co/relbert">https://huggingface.co/relbert</a></li>
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  <li>&nbsp; - PyPI: <a href="https://pypi.org/project/relbert">https://pypi.org/project/relbert</a></li>
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  </ul>