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@@ -19,7 +19,7 @@ This is a fine-tuned BERT-based language model to classify NLP-related research
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It is a multi-label classifier that can predict concepts from all levels of the NLP taxonomy.
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If the model identifies a lower-level concept, it did learn to predict both the lower-level concept and its hypernyms in the NLP taxonomy.
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The model is fine-tuned on a weakly labeled dataset of 178,521 scientific papers from the ACL Anthology, the arXiv cs.CL domain, and Scopus.
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Prior to fine-tuning, the model is initialized with weights from [allenai/
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📄 Paper: [Exploring the Landscape of Natural Language Processing Research (RANLP 2023)](https://arxiv.org/abs/2307.10652).
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It is a multi-label classifier that can predict concepts from all levels of the NLP taxonomy.
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If the model identifies a lower-level concept, it did learn to predict both the lower-level concept and its hypernyms in the NLP taxonomy.
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The model is fine-tuned on a weakly labeled dataset of 178,521 scientific papers from the ACL Anthology, the arXiv cs.CL domain, and Scopus.
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Prior to fine-tuning, the model is initialized with weights from [allenai/specter2_base](https://huggingface.co/allenai/specter2_base).
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📄 Paper: [Exploring the Landscape of Natural Language Processing Research (RANLP 2023)](https://arxiv.org/abs/2307.10652).
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