|
--- |
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inference: false |
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datasets: |
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- answerdotai/MMARCO-japanese-32-scored-triplets |
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- unicamp-dl/mmarco |
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language: |
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- ja |
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pipeline_tag: sentence-similarity |
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tags: |
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- ColBERT |
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base_model: |
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- cl-tohoku/bert-base-japanese-v3 |
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- bclavie/JaColBERT |
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license: mit |
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library_name: RAGatouille |
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--- |
|
|
|
Model weights for the JaColBERTv2.4 checkpoint, which is the pre-post-training version of JaColBERTv2.5, using an entirely overhauled training recipe and trained on just 40% of the data of JaColBERTv2. |
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|
|
This model largely outperforms all previous approaches, including JaColBERTV2 multilingual models such as BGE-M3, on all datasets. |
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|
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This page will be updated with the full details and the model report in the next few days. |
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|
|
``` |
|
@misc{clavié2024jacolbertv25optimisingmultivectorretrievers, |
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title={JaColBERTv2.5: Optimising Multi-Vector Retrievers to Create State-of-the-Art Japanese Retrievers with Constrained Resources}, |
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author={Benjamin Clavié}, |
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year={2024}, |
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eprint={2407.20750}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.IR}, |
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url={https://arxiv.org/abs/2407.20750}, |
|
} |
|
``` |
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|
