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Browse files- README.md +33 -0
- mt5xxl-monot5-mmarco-engfas.jsonl.gz +3 -0
- mt5xxl-monot5-mmarco-engrus.jsonl.gz +3 -0
- mt5xxl-monot5-mmarco-engzho.jsonl.gz +3 -0
- mt5xxl-monot5-mmarco-fasfas.jsonl.gz +3 -0
- mt5xxl-monot5-mmarco-rusrus.jsonl.gz +3 -0
- mt5xxl-monot5-mmarco-zhozho.jsonl.gz +3 -0
- t53b-monot5-msmarco-engeng.jsonl.gz +3 -0
README.md
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---
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license: mit
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---
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---
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license: mit
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# MS MARCO Distillation Scores for Translate-Distill
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This repository contains [MS MARCO](https://microsoft.github.io/msmarco/) training
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query-passage scores produced by MonoT5 reranker
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[`unicamp-dl/mt5-13b-mmarco-100k`](https://huggingface.co/unicamp-dl/mt5-13b-mmarco-100k) and
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[`castorini/monot5-3b-msmarco-10k`](https://huggingface.co/castorini/monot5-3b-msmarco-10k).
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Each training query is associated with the top-50 passages retrieved by the [ColBERTv2](https://arxiv.org/abs/2112.01488) model.
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Files are gzip compressed and with the naming scheme of `{teacher}-monot5-{msmarco, mmarco}-{qlang}{plang}.jsonl.gz`,
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which indicates the teacher reranker that inferenced using `qlang` queries and `plang` passages from MS MARCO.
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For languages other than English (eng), we use the translated text provided by mmarco and [neuMarco](https://ir-datasets.com/neumarco.html).
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## Usage
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We recommand downloading the files to incorporate with the training script in the [PLAID-X](https://github.com/hltcoe/ColBERT-X/tree/plaid-x) codebase.
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## Citation and Bibtex Info
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Please cite the following paper if you use the scores.
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```bibtext
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@inproceedings{translate-distill,
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author = {Eugene Yang and Dawn Lawrie and James Mayfield and Douglas W. Oard and Scott Miller},
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title = {Translate-Distill: Learning Cross-Language \ Dense Retrieval by Translation and Distillation},
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booktitle = {Proceedings of the 46th European Conference on Information Retrieval (ECIR)},
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year = {2024},
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url = {tba}
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}
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```
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mt5xxl-monot5-mmarco-engfas.jsonl.gz
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