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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  license: mit
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ language:
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+ - en
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+ - zh
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+ tags:
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+ - clir
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+ - colbertx
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+ - plaidx
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+ - xlm-roberta-large
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+ datasets:
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+ - ms_marco
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+ - eugene-yang/tdist-msmarco-scores
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+ task_categories:
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+ - text-retrieval
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+ - information-retrieval
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+ task_ids:
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+ - passage-retrieval
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+ - cross-language-retrieval
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  license: mit
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  ---
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+
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+ # ColBERT-X for English-Chinese CLIR using Translate-Distill
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+
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+ ## Model Description
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+
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+ Translate-Distill is a training technique that produces state-of-the-art CLIR dense retrieval model through translation and distillation.
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+ `plaidx-large-zho-tdist-t53b-engeng` is trained with KL-Divergence from the t53b MonoT5 reranker inferenced on
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+ English MS MARCO training queries and English passages.
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+
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+ ### Teacher Models:
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+
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+ - `t53b`: [`castorini/monot5-3b-msmarco-10k`](https://huggingface.co/castorini/monot5-3b-msmarco-10k)
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+ - `mt5xxl`: [`unicamp-dl/mt5-13b-mmarco-100k`](https://huggingface.co/unicamp-dl/mt5-13b-mmarco-100k)
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+
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+ ### Training Parameters
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+
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+ - learning rate: 5e-6
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+ - update steps: 200,000
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+ - nway (number of passages per query): 6 (randomly selected from 50)
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+ - per device batch size (number of query-passage set): 8
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+ - training GPU: 8 NVIDIA V100 with 32 GB memory
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+
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+ ## Usage
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+
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+ To properly load ColBERT-X models from Huggingface Hub, please use the following version of PLAID-X.
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+ ```bash
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+ pip install git+https://github.com/hltcoe/ColBERT-X.git@plaid-x
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+ ```
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+
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+ Following code snippet loads the model through Huggingface API.
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+ ```python
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+ from colbert.modeling.checkpoint import Checkpoint
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+ from colbert.infra import ColBERTConfig
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+
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+ Checkpoint('plaidx-large-zho-tdist-t53b-engeng', colbert_config=ColBERTConfig())
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+ ```
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+
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+ For full tutorial, please refer to the [PLAID-X Jupyter Notebook](https://colab.research.google.com/github/hltcoe/clir-tutorial/blob/main/notebooks/clir_tutorial_plaidx.ipynb),
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+ which is part of the [SIGIR 2023 CLIR Tutorial](https://github.com/hltcoe/clir-tutorial).
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+
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+ ## BibTeX entry and Citation Info
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+
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+ Please cite the following two papers if you use the model.
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+
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+
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+ ```bibtex
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+ @inproceedings{colbert-x,
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+ author = {Suraj Nair and Eugene Yang and Dawn Lawrie and Kevin Duh and Paul McNamee and Kenton Murray and James Mayfield and Douglas W. Oard},
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+ title = {Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models},
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+ booktitle = {Proceedings of the 44th European Conference on Information Retrieval (ECIR)},
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+ year = {2022},
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+ url = {https://arxiv.org/abs/2201.08471}
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+ }
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+ ```
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+
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+ ```bibtex
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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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+ "triples": "\/expscratch\/eyang\/workspace\/plaid-aux\/training_triples\/msmarco-passages\/triples_t53b-monot5-msmarco-eng.jsonl",
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+ "current_datetime": "Sep 16, 2023 ; 1:37PM EDT (-0400)",
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+ "cmd": "train.py --model_name xlm-roberta-large --training_triples \/expscratch\/eyang\/workspace\/plaid-aux\/training_triples\/msmarco-passages\/triples_t53b-monot5-msmarco-eng.jsonl --training_irds_id neumarco\/zh\/train --maxsteps 200000 --learning_rate 5e-6 --kd_loss KLD --per_device_batch_size 8 --nway 6 --run_tag zho-KLD-shuf-5e-6\/t53b-monot5-msmarco-eng --experiment plaid_xlm-roberta-large_fixeddp",
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+ "version": "colbert-v0.4"
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+ }
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+ }
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