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--- |
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title: README |
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emoji: 🔥 |
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colorFrom: red |
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colorTo: indigo |
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sdk: static |
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pinned: false |
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--- |
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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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<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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pip install relbert |
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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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from relbert import RelBERT |
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model = RelBERT('relbert/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> - GitHub: <a href="https://github.com/asahi417/relbert">https://github.com/asahi417/relbert</a></li> |
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<li> - 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> - HuggingFace: <a href="https://huggingface.co/relbert">https://huggingface.co/relbert</a></li> |
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<li> - PyPI: <a href="https://pypi.org/project/relbert">https://pypi.org/project/relbert</a></li> |
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</ul> |
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