transformers pytorch safetensors xlm-roberta clir colbertx plaidx xlm-roberta-large en ru dataset:ms_marco dataset:hltcoe/tdist-msmarco-scores arxiv:2201.08471 arxiv:2401.04810 license:mit endpoints_compatible region:us
---
datasets:
- ms_marco
- hltcoe/tdist-msmarco-scores
language:
- en
- ru
license: mit
tags:
- clir
- colbertx
- plaidx
- xlm-roberta-large
task_categories:
- text-retrieval
- information-retrieval
task_ids:
- passage-retrieval
- cross-language-retrieval
---

# ColBERT-X for English-Russian CLIR using Translate-Distill

## CLIR Model Setting

- Query language: English
- Query length: 32 token max
- Document language: Russian
- Document length: 180 token max (please use MaxP to aggregate the passage score if needed)

## Model Description

Translate-Distill is a training technique that produces state-of-the-art CLIR dense retrieval model through translation and distillation.
`plaidx-large-rus-tdist-mt5xxl-engrus` is trained with KL-Divergence from the mt5xxl MonoT5 reranker inferenced on 
English MS MARCO training queries and Russian translated passages. 

### Teacher Models:

- `t53b`: [`castorini/monot5-3b-msmarco-10k`](https://huggingface.co/castorini/monot5-3b-msmarco-10k)
- `mt5xxl`: [`unicamp-dl/mt5-13b-mmarco-100k`](https://huggingface.co/unicamp-dl/mt5-13b-mmarco-100k)

### Training Parameters

- learning rate: 5e-6
- update steps: 200,000
- nway (number of passages per query): 6 (randomly selected from 50)
- per device batch size (number of query-passage set): 8
- training GPU: 8 NVIDIA V100 with 32 GB memory

## Usage

To properly load ColBERT-X models from Huggingface Hub, please use the following version of PLAID-X. 
```bash
pip install PLAID-X==0.3.1
```

Following code snippet loads the model through Huggingface API. 
```python
from colbert.modeling.checkpoint import Checkpoint
from colbert.infra import ColBERTConfig

Checkpoint('hltcoe/plaidx-large-rus-tdist-mt5xxl-engrus', colbert_config=ColBERTConfig())
```

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), 
which is part of the [SIGIR 2023 CLIR Tutorial](https://github.com/hltcoe/clir-tutorial). 

## BibTeX entry and Citation Info

Please cite the following two papers if you use the model. 


```bibtex
@inproceedings{colbert-x,
    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},
    title = {Transfer Learning Approaches for Building Cross-Language Dense Retrieval Models},
    booktitle = {Proceedings of the 44th European Conference on Information Retrieval (ECIR)},
    year = {2022},
    url = {https://arxiv.org/abs/2201.08471}
}
```

```bibtex
@inproceedings{translate-distill,
    author = {Eugene Yang and Dawn Lawrie and James Mayfield and Douglas W. Oard and Scott Miller},
    title = {Translate-Distill: Learning Cross-Language Dense Retrieval by Translation and Distillation},
    booktitle = {Proceedings of the 46th European Conference on Information Retrieval (ECIR)},
    year = {2024},
    url = {https://arxiv.org/abs/2401.04810}
}
```
