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---
license: apache-2.0
language:
- en
tags:
- ColBERT
- RAGatouille
- passage-retrieval
---
# answerai-colbert-small-v1
**answerai-colbert-small-v1** is a new, proof-of-concept model by [Answer.AI](https://answer.ai), showing the strong performance multi-vector models with the new [JaColBERTv2.5 training recipe](https://arxiv.org/abs/2407.20750) and some extra tweaks can reach, even with just **33 million parameters**.
While being MiniLM-sized, it outperforms all previous similarly-sized models on common benchmarks, and even outperforms much larger popular models such as e5-large-v2 or bge-base-en-v1.5.
For more information about this model or how it was trained, head over to the [announcement blogpost](https://www.answer.ai/posts/2024-08-13-small-but-mighty-colbert.html).
## Usage
### Installation
This model was designed with the upcoming RAGatouille overhaul in mind. However, it's compatible with all recent ColBERT implementations!
To use it, you can either use the Stanford ColBERT library, or RAGatouille. You can install both or either by simply running.
```sh
pip install --upgrade ragatouille
pip install --upgrade colbert-ai
```
If you're interested in using this model as a re-ranker (it vastly outperforms cross-encoders its size!), you can do so via the [rerankers](https://github.com/AnswerDotAI/rerankers) library:
```sh
pip install --upgrade rerankers[transformers]
```
### Rerankers
```python
from rerankers import Reranker
ranker = Reranker("answerdotai/answerai-colbert-small-v1", model_type='colbert')
docs = ['Hayao Miyazaki is a Japanese director, born on [...]', 'Walt Disney is an American author, director and [...]', ...]
query = 'Who directed spirited away?'
ranker.rank(query=query, docs=docs)
```
### RAGatouille
```python
from ragatouille import RAGPretrainedModel
RAG = RAGPretrainedModel.from_pretrained("answerdotai/answerai-colbert-small-v1")
docs = ['Hayao Miyazaki is a Japanese director, born on [...]', 'Walt Disney is an American author, director and [...]', ...]
RAG.index(docs, index_name="ghibli")
query = 'Who directed spirited away?'
results = RAG.search(query)
```
### Stanford ColBERT
#### Indexing
```python
from colbert import Indexer
from colbert.infra import Run, RunConfig, ColBERTConfig
INDEX_NAME = "DEFINE_HERE"
if __name__ == "__main__":
config = ColBERTConfig(
doc_maxlen=512,
nbits=2
)
indexer = Indexer(
checkpoint="answerdotai/answerai-colbert-small-v1",
config=config,
)
docs = ['Hayao Miyazaki is a Japanese director, born on [...]', 'Walt Disney is an American author, director and [...]', ...]
indexer.index(name=INDEX_NAME, collection=docs)
```
#### Querying
```python
from colbert import Searcher
from colbert.infra import Run, RunConfig, ColBERTConfig
INDEX_NAME = "THE_INDEX_YOU_CREATED"
k = 10
if __name__ == "__main__":
config = ColBERTConfig(
query_maxlen=32 # Adjust as needed, we recommend the nearest higher multiple of 16 to your query
)
searcher = Searcher(
index=index_name,
config=config
)
query = 'Who directed spirited away?'
results = searcher.search(query, k=k)
```
#### Extracting Vectors
Finally, if you want to extract individula vectors, you can use the model this way:
```python
from colbert.modeling.checkpoint import Checkpoint
ckpt = Checkpoint("answerdotai/answerai-colbert-small-v1", colbert_config=ColBERTConfig())
embedded_query = ckpt.queryFromText(["Who dubs Howl's in English?"], bsize=16)
```
## Results
### Against single-vector models
| Dataset / Model | answer-colbert-s | snowflake-s | bge-small-en | bge-base-en |
|:-----------------|:-----------------:|:-------------:|:-------------:|:-------------:|
| **Size** | 33M (1x) | 33M (1x) | 33M (1x) | **109M (3.3x)** |
| **BEIR AVG** | **53.79** | 51.99 | 51.68 | 53.25 |
| **FiQA2018** | **41.15** | 40.65 | 40.34 | 40.65 |
| **HotpotQA** | **76.11** | 66.54 | 69.94 | 72.6 |
| **MSMARCO** | **43.5** | 40.23 | 40.83 | 41.35 |
| **NQ** | **59.1** | 50.9 | 50.18 | 54.15 |
| **TRECCOVID** | **84.59** | 80.12 | 75.9 | 78.07 |
| **ArguAna** | 50.09 | 57.59 | 59.55 | **63.61** |
| **ClimateFEVER**| 33.07 | **35.2** | 31.84 | 31.17 |
| **CQADupstackRetrieval** | 38.75 | 39.65 | 39.05 | **42.35** |
| **DBPedia** | **45.58** | 41.02 | 40.03 | 40.77 |
| **FEVER** | **90.96** | 87.13 | 86.64 | 86.29 |
| **NFCorpus** | 37.3 | 34.92 | 34.3 | **37.39** |
| **QuoraRetrieval** | 87.72 | 88.41 | 88.78 | **88.9** |
| **SCIDOCS** | 18.42 | **21.82** | 20.52 | 21.73 |
| **SciFact** | **74.77** | 72.22 | 71.28 | 74.04 |
| **Touche2020** | 25.69 | 23.48 | **26.04** | 25.7 |
### Against ColBERTv2.0
| Dataset / Model | answerai-colbert-small-v1 | ColBERTv2.0 |
|:-----------------|:-----------------------:|:------------:|
| **BEIR AVG** | **53.79** | 50.02 |
| **DBPedia** | **45.58** | 44.6 |
| **FiQA2018** | **41.15** | 35.6 |
| **NQ** | **59.1** | 56.2 |
| **HotpotQA** | **76.11** | 66.7 |
| **NFCorpus** | **37.3** | 33.8 |
| **TRECCOVID** | **84.59** | 73.3 |
| **Touche2020** | 25.69 | **26.3** |
| **ArguAna** | **50.09** | 46.3 |
| **ClimateFEVER**| **33.07** | 17.6 |
| **FEVER** | **90.96** | 78.5 |
| **QuoraRetrieval** | **87.72** | 85.2 |
| **SCIDOCS** | **18.42** | 15.4 |
| **SciFact** | **74.77** | 69.3 |
## Referencing
We'll most likely eventually release a technical report. In the meantime, if you use this model or other models following the JaColBERTv2.5 recipe and would like to give us credit, please cite the JaColBERTv2.5 journal pre-print:
```
@article{clavie2024jacolbertv2,
title={JaColBERTv2.5: Optimising Multi-Vector Retrievers to Create State-of-the-Art Japanese Retrievers with Constrained Resources},
author={Clavi{\'e}, Benjamin},
journal={arXiv preprint arXiv:2407.20750},
year={2024}
}
```
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