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---
license: cc-by-nc-4.0
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
inference: false
tags:
- ColBERT
- passage-retrieval
---
<br><br>
<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>
<p align="center">
<b>Trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b>
</p>
<p align="center">
<b>JinaColBERT V2: A General-Purpose Multilingual Late Interaction Retriever.</b>
</p>
JinaColBERT V2 (`jina-colbert-v2`) is a new model based on the [JinaColBERT V1](https://jina.ai/news/what-is-colbert-and-late-interaction-and-why-they-matter-in-search/) that expands on the capabilities and performance of the [`jina-colbert-v1-en`](https://huggingface.co/jinaai/jina-colbert-v1-en) model. Like the previous release, it has Jina AI’s 8192 token input context and the [improved efficiency, performance](https://jina.ai/news/what-is-colbert-and-late-interaction-and-why-they-matter-in-search/), and [explainability](https://jina.ai/news/ai-explainability-made-easy-how-late-interaction-makes-jina-colbert-transparent/) of token-level embeddings and late interaction.
This new release adds new functionality and performance improvements:
- Multilingual support for dozens of languages, with strong performance on major global languages.
- [Matryoshka embeddings](https://huggingface.co/blog/matryoshka), which allow users to trade between efficiency and precision flexibly.
- Superior retrieval performance when compared to the English-only [`jina-colbert-v1-en`](https://huggingface.co/jinaai/jina-colbert-v1-en).
JinaColBERT V2 offers three different versions for different embeddings dimensions:
[`jinaai/jina-colbert-v2`](https://huggingface.co/jinaai/jina-colbert-v2): 128 dimension embeddings
[`jinaai/jina-colbert-v2-96`](https://huggingface.co/jinaai/jina-colbert-v2-96): 96 dimension embeddings
[`jinaai/jina-colbert-v2-64`](https://huggingface.co/jinaai/jina-colbert-v2-64): 64 dimension embeddings
## Usage
### Installation
`jina-colbert-v2` is trained with flash attention and therefore requires `einops` and `flash_attn` to be installed.
To use the model, you could either use the Standford ColBERT library or use the `pylate`/`ragatouille` package that we provide.
```bash
pip install -U einops flash_attn
pip install -U ragatouille # or
pip install -U colbert-ai # or
pip install -U pylate
```
### PyLate
```python
# Please refer to Pylate: https://github.com/lightonai/pylate for detailed usage
from pylate import indexes, models, retrieve
model = models.ColBERT(
model_name_or_path="jinaai/jina-colbert-v2",
query_prefix="[QueryMarker]",
document_prefix="[DocumentMarker]",
attend_to_expansion_tokens=True,
trust_remote_code=True,
)
```
### RAGatouille
```python
from ragatouille import RAGPretrainedModel
RAG = RAGPretrainedModel.from_pretrained("jinaai/jina-colbert-v2")
docs = [
"ColBERT is a novel ranking model that adapts deep LMs for efficient retrieval.",
"Jina-ColBERT is a ColBERT-style model but based on JinaBERT so it can support both 8k context length, fast and accurate retrieval.",
]
RAG.index(docs, index_name="demo")
query = "What does ColBERT do?"
results = RAG.search(query)
```
### Stanford ColBERT
```python
from colbert.infra import ColBERTConfig
from colbert.modeling.checkpoint import Checkpoint
ckpt = Checkpoint("jinaai/jina-colbert-v2", colbert_config=ColBERTConfig())
docs = [
"ColBERT is a novel ranking model that adapts deep LMs for efficient retrieval.",
"Jina-ColBERT is a ColBERT-style model but based on JinaBERT so it can support both 8k context length, fast and accurate retrieval.",
]
query_vectors = ckpt.queryFromText(docs, bsize=2)
```
## Evaluation Results
### Retrieval Benchmarks
#### BEIR
| **NDCG@10** | **jina-colbert-v2** | **jina-colbert-v1** | **ColBERTv2.0** | **BM25** |
|--------------------|---------------------|---------------------|-----------------|----------|
| **avg** | 0.531 | 0.502 | 0.496 | 0.440 |
| **nfcorpus** | 0.346 | 0.338 | 0.337 | 0.325 |
| **fiqa** | 0.408 | 0.368 | 0.354 | 0.236 |
| **trec-covid** | 0.834 | 0.750 | 0.726 | 0.656 |
| **arguana** | 0.366 | 0.494 | 0.465 | 0.315 |
| **quora** | 0.887 | 0.823 | 0.855 | 0.789 |
| **scidocs** | 0.186 | 0.169 | 0.154 | 0.158 |
| **scifact** | 0.678 | 0.701 | 0.689 | 0.665 |
| **webis-touche** | 0.274 | 0.270 | 0.260 | 0.367 |
| **dbpedia-entity** | 0.471 | 0.413 | 0.452 | 0.313 |
| **fever** | 0.805 | 0.795 | 0.785 | 0.753 |
| **climate-fever** | 0.239 | 0.196 | 0.176 | 0.213 |
| **hotpotqa** | 0.766 | 0.656 | 0.675 | 0.603 |
| **nq** | 0.640 | 0.549 | 0.524 | 0.329 |
#### MS MARCO Passage Retrieval
| **MRR@10** | **jina-colbert-v2** | **jina-colbert-v1** | **ColBERTv2.0** | **BM25** |
|-------------|---------------------|---------------------|-----------------|----------|
| **MSMARCO** | 0.396 | 0.390 | 0.397 | 0.187 |
### Multilingual Benchmarks
#### MIRACLE
| **NDCG@10** | **jina-colbert-v2** | **mDPR (zero shot)** |
|---------|---------------------|----------------------|
| **avg** | 0.627 | 0.427 |
| **ar** | 0.753 | 0.499 |
| **bn** | 0.750 | 0.443 |
| **de** | 0.504 | 0.490 |
| **es** | 0.538 | 0.478 |
| **en** | 0.570 | 0.394 |
| **fa** | 0.563 | 0.480 |
| **fi** | 0.740 | 0.472 |
| **fr** | 0.541 | 0.435 |
| **hi** | 0.600 | 0.383 |
| **id** | 0.547 | 0.272 |
| **ja** | 0.632 | 0.439 |
| **ko** | 0.671 | 0.419 |
| **ru** | 0.643 | 0.407 |
| **sw** | 0.499 | 0.299 |
| **te** | 0.742 | 0.356 |
| **th** | 0.772 | 0.358 |
| **yo** | 0.623 | 0.396 |
| **zh** | 0.523 | 0.512 |
#### mMARCO
| **MRR@10** | **jina-colbert-v2** | **BM-25** | **ColBERT-XM** |
|------------|---------------------|-----------|----------------|
| **avg** | 0.313 | 0.141 | 0.254 |
| **ar** | 0.272 | 0.111 | 0.195 |
| **de** | 0.331 | 0.136 | 0.270 |
| **nl** | 0.330 | 0.140 | 0.275 |
| **es** | 0.341 | 0.158 | 0.285 |
| **fr** | 0.335 | 0.155 | 0.269 |
| **hi** | 0.309 | 0.134 | 0.238 |
| **id** | 0.319 | 0.149 | 0.263 |
| **it** | 0.337 | 0.153 | 0.265 |
| **ja** | 0.276 | 0.141 | 0.241 |
| **pt** | 0.337 | 0.152 | 0.276 |
| **ru** | 0.298 | 0.124 | 0.251 |
| **vi** | 0.287 | 0.136 | 0.226 |
| **zh** | 0.302 | 0.116 | 0.246 |
### Matryoshka Representation Benchmarks
#### BEIR
| **NDCG@10** | **dim=128** | **dim=96** | **dim=64** |
|----------------|-------------|------------|------------|
| **avg** | 0.599 | 0.591 | 0.589 |
| **nfcorpus** | 0.346 | 0.340 | 0.347 |
| **fiqa** | 0.408 | 0.404 | 0.404 |
| **trec-covid** | 0.834 | 0.808 | 0.805 |
| **hotpotqa** | 0.766 | 0.764 | 0.756 |
| **nq** | 0.640 | 0.640 | 0.635 |
#### MSMARCO
| **MRR@10** | **dim=128** | **dim=96** | **dim=64** |
|----------------|-------------|------------|------------|
| **msmarco** | 0.396 | 0.391 | 0.388 |
## Other Models
Additionally, we provide the following embedding models, you can also use them for retrieval.
- [`jina-embeddings-v2-base-en`](https://huggingface.co/jinaai/jina-embeddings-v2-base-en): 137 million parameters.
- [`jina-embeddings-v2-base-zh`](https://huggingface.co/jinaai/jina-embeddings-v2-base-zh): 161 million parameters Chinese-English bilingual model.
- [`jina-embeddings-v2-base-de`](https://huggingface.co/jinaai/jina-embeddings-v2-base-de): 161 million parameters German-English bilingual model.
- [`jina-embeddings-v2-base-es`](https://huggingface.co/jinaai/jina-embeddings-v2-base-es): 161 million parameters Spanish-English bilingual model.
- [`jina-reranker-v2`](https://huggingface.co/jinaai/jina-reranker-v2-base-multilingual): multilingual reranker model.
- [`jina-clip-v1`](https://huggingface.co/jinaai/jina-clip-v1): English multimodal (text-image) embedding model.
## Contact
Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
```
@inproceedings{xiao-etal-2024-jina,
title = "{J}ina-{C}ol{BERT}-v2: A General-Purpose Multilingual Late Interaction Retriever",
author = {Jha, Rohan and
Wang, Bo and
G{\"u}nther, Michael and
Mastrapas, Georgios and
Sturua, Saba and
Mohr, Isabelle and
Koukounas, Andreas and
Wang, Mohammad Kalim and
Wang, Nan and
Xiao, Han},
editor = {S{\"a}lev{\"a}, Jonne and
Owodunni, Abraham},
booktitle = "Proceedings of the Fourth Workshop on Multilingual Representation Learning (MRL 2024)",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.mrl-1.11/",
doi = "10.18653/v1/2024.mrl-1.11",
pages = "159--166",
abstract = "Multi-vector dense models, such as ColBERT, have proven highly effective in information retrieval. ColBERT`s late interaction scoring approximates the joint query-document attention seen in cross-encoders while maintaining inference efficiency closer to traditional dense retrieval models, thanks to its bi-encoder architecture and recent optimizations in indexing and search. In this paper, we introduce a novel architecture and a training framework to support long context window and multilingual retrieval. Leveraging Matryoshka Representation Loss, we further demonstrate that the reducing the embedding dimensionality from 128 to 64 has insignificant impact on the model`s retrieval performance and cut storage requirements by up to 50{\%}. Our new model, Jina-ColBERT-v2, demonstrates strong performance across a range of English and multilingual retrieval tasks,"
}
```
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