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#AdaSent

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UKP Lab<p>You can find our paper here:<br />📃 <a href="https://arxiv.org/abs/2311.00408" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://</span><span class="">arxiv.org/abs/2311.00408</span><span class="invisible"></span></a> <br />and our code here:<br />💻 <a href="https://github.com/UKPLab/AdaSent" target="_blank" rel="nofollow noopener" translate="no"><span class="invisible">https://</span><span class="">github.com/UKPLab/AdaSent</span><span class="invisible"></span></a></p><p>Check out the work of our authors Yongxin Huang, Kexin Wang, Sourav Dutta, Raj Nath Patel, Goran Glavaš and Iryna Gurevych! (6/🧵) <a href="https://sigmoid.social/tags/EMNLP2023" class="mention hashtag" rel="tag">#<span>EMNLP2023</span></a> <a href="https://sigmoid.social/tags/AdaSent" class="mention hashtag" rel="tag">#<span>AdaSent</span></a> <a href="https://sigmoid.social/tags/NLProc" class="mention hashtag" rel="tag">#<span>NLProc</span></a></p>
UKP Lab<p>Need a lightweight solution for few-shot domain-specific sentence classification?</p><p>We propose <a href="https://sigmoid.social/tags/AdaSent" class="mention hashtag" rel="tag">#<span>AdaSent</span></a>!<br />🚀 Up to 7.2 acc. gain in 8-shot classification with 10K unlabeled data<br />🪶 Small backbone with 82M parameters<br />🧩 Reusable general sentence adapter across domains<br />(1/🧵) <a href="https://sigmoid.social/tags/EMNLP2023" class="mention hashtag" rel="tag">#<span>EMNLP2023</span></a></p>
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