--- 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). ## Architecture ## 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(documents, 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 ![](https://www.answer.ai/posts/images/minicolbert/small_results.png) | 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} } ```