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  <img src="https://raw.githubusercontent.com/asahi417/relbert/test/assets/relbert_logo.png" alt="" width="150" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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- 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 <b> pip install relbert</b> and explore in python as below.
 
 
 
 
 
 
 
 
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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><a href="https://arxiv.org/abs/2110.15705">RelBERT paper (EMNLP 2021 main conference) </a></li>
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- <li><a href="https://github.com/asahi417/relbert">GitHub</a></li>
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- <li><a href="https://pypi.org/project/relbert">pip</a></li>
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  </ul>
 
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  <img src="https://raw.githubusercontent.com/asahi417/relbert/test/assets/relbert_logo.png" alt="" width="150" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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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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+
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+ <pre class="line-numbers">
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+ <code class="language-python">
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+ pip install relbert
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+ </code>
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+ </pre>
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+
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+ and explore in python as below.
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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>\t <a href="https://arxiv.org/abs/2110.15705">RelBERT paper (EMNLP 2021 main conference) </a></li>
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+ <li>\t <a href="https://github.com/asahi417/relbert">GitHub</a></li>
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+ <li>\t <a href="https://pypi.org/project/relbert">pip</a></li>
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  </ul>