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---
license: cc-by-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
tags:
- ColBERT
- passage-retrieval
---
<br><br>
<p align="center">
<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." 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: your 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 `ragatouille` package that we provide.
```bash
pip install -U einops flash_attn
pip install -U ragatouille
pip install -U colbert-ai
```
### 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 | | 0.246 |
### 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. |