Beyond One-Size-Fits-All: Multi-Domain, Multi-Task Framework for Embedding Model Selection
Fuente:
arXiv
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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916375141810176 |
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| author | Khetan, Vivek |
| author_facet | Khetan, Vivek |
| contents | This position paper proposes a systematic approach towards developing a framework to help select the most effective embedding models for natural language processing (NLP) tasks, addressing the challenge posed by the proliferation of both proprietary and open-source encoder models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00458 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Beyond One-Size-Fits-All: Multi-Domain, Multi-Task Framework for Embedding Model Selection Khetan, Vivek Computation and Language Information Retrieval This position paper proposes a systematic approach towards developing a framework to help select the most effective embedding models for natural language processing (NLP) tasks, addressing the challenge posed by the proliferation of both proprietary and open-source encoder models. |
| title | Beyond One-Size-Fits-All: Multi-Domain, Multi-Task Framework for Embedding Model Selection |
| topic | Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2404.00458 |