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- <img src="https://raw.githubusercontent.com/asahi417/relbert/test/assets/relbert_logo.png" alt="" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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- RelBERT: High-quality semantic representative embedding of word pairs powered by pre-trained language model.
 
 
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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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+ <h1>RelBERT</h1>
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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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+ from relbert import RelBERT
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+ model = RelBERT('asahi417/relbert-roberta-large')
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+ vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
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+ </code>
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+ </pre>
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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>