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answerai-colbert-small-v1

sentence-transformers
ONNX
Safetensors
English
bert
ColBERT
multi-vector
RAGatouille
passage-retrieval
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@@ -16,52 +16,6 @@ While being MiniLM-sized, it outperforms all previous similarly-sized models on
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  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).
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- ## Results
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-
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- ### Against single-vector models
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-
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- ![](https://www.answer.ai/posts/images/minicolbert/small_results.png)
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-
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-
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- | Dataset / Model | answer-colbert-s | snowflake-s | bge-small-en | bge-base-en |
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- |:-----------------|:-----------------:|:-------------:|:-------------:|:-------------:|
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- | **Size** | 33M (1x) | 33M (1x) | 33M (1x) | **109M (3.3x)** |
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- | **BEIR AVG** | **53.79** | 51.99 | 51.68 | 53.25 |
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- | **FiQA2018** | **41.15** | 40.65 | 40.34 | 40.65 |
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- | **HotpotQA** | **76.11** | 66.54 | 69.94 | 72.6 |
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- | **MSMARCO** | **43.5** | 40.23 | 40.83 | 41.35 |
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- | **NQ** | **59.1** | 50.9 | 50.18 | 54.15 |
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- | **TRECCOVID** | **84.59** | 80.12 | 75.9 | 78.07 |
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- | **ArguAna** | 50.09 | 57.59 | 59.55 | **63.61** |
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- | **ClimateFEVER**| **33.07** | 35.2 | 31.84 | 31.17 |
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- | **CQADupstackRetrieval** | 38.75 | 39.65 | 39.05 | **42.35** |
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- | **DBPedia** | **45.58** | 41.02 | 40.03 | 40.77 |
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- | **FEVER** | **90.96** | 87.13 | 86.64 | 86.29 |
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- | **NFCorpus** | **37.3** | 34.92 | 34.3 | 37.39 |
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- | **QuoraRetrieval** | 87.72 | 88.41 | **88.78** | 88.9 |
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- | **SCIDOCS** | 18.42 | **21.82** | 20.52 | 21.73 |
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- | **SciFact** | **74.77** | 72.22 | 71.28 | 74.04 |
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- | **Touche2020** | 25.69 | 23.48 | **26.04** | 25.7 |
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-
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- ### Against ColBERTv2.0
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-
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- | Dataset / Model | answerai-colbert-small-v1 | ColBERTv2.0 |
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- |:-----------------|:-----------------------:|:------------:|
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- | **BEIR AVG** | **53.79** | 50.02 |
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- | **DBPedia** | **45.58** | 44.6 |
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- | **FiQA2018** | **41.15** | 35.6 |
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- | **NQ** | **59.1** | 56.2 |
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- | **HotpotQA** | **76.11** | 66.7 |
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- | **NFCorpus** | **37.3** | 33.8 |
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- | **TRECCOVID** | **84.59** | 73.3 |
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- | **Touche2020** | 25.69 | **26.3** |
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- | **ArguAna** | **50.09** | 46.3 |
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- | **ClimateFEVER**| **33.07** | 17.6 |
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- | **FEVER** | **90.96** | 78.5 |
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- | **QuoraRetrieval** | **87.72** | 85.2 |
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- | **SCIDOCS** | **18.42** | 15.4 |
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- | **SciFact** | **74.77** | 69.3 |
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-
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  ## Usage
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  ### Installation
@@ -93,6 +47,19 @@ ranker.rank(query=query, docs=docs)
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  ### RAGatouille
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  ### Stanford ColBERT
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  #### Indexing
@@ -149,4 +116,51 @@ from colbert.modeling.checkpoint import Checkpoint
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  ckpt = Checkpoint(answerdotai/answerai-colbert-small-v1", colbert_config=ColBERTConfig())
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  embedded_query = ckpt.queryFromText(["Who dubs Howl's in English?"], bsize=16)
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- ```
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  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).
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  ## Usage
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  ### Installation
 
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  ### RAGatouille
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+ ```python
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+ from ragatouille import RAGPretrainedModel
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+ RAG = RAGPretrainedModel.from_pretrained("answerdotai/answerai-colbert-small-v1")
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+ docs = ['Hayao Miyazaki is a Japanese director, born on [...]', 'Walt Disney is an American author, director and [...]', ...]
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+ RAG.index(documents, index_name="ghibli")
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+
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+ query = 'Who directed spirited away?'
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+ results = RAG.search(query)
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+ ```
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+
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  ### Stanford ColBERT
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  #### Indexing
 
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  ckpt = Checkpoint(answerdotai/answerai-colbert-small-v1", colbert_config=ColBERTConfig())
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  embedded_query = ckpt.queryFromText(["Who dubs Howl's in English?"], bsize=16)
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+ ```
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+
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+ ## Results
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+
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+ ### Against single-vector models
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+ ![](https://www.answer.ai/posts/images/minicolbert/small_results.png)
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+ | Dataset / Model | answer-colbert-s | snowflake-s | bge-small-en | bge-base-en |
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+ |:-----------------|:-----------------:|:-------------:|:-------------:|:-------------:|
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+ | **Size** | 33M (1x) | 33M (1x) | 33M (1x) | **109M (3.3x)** |
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+ | **BEIR AVG** | **53.79** | 51.99 | 51.68 | 53.25 |
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+ | **FiQA2018** | **41.15** | 40.65 | 40.34 | 40.65 |
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+ | **HotpotQA** | **76.11** | 66.54 | 69.94 | 72.6 |
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+ | **MSMARCO** | **43.5** | 40.23 | 40.83 | 41.35 |
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+ | **NQ** | **59.1** | 50.9 | 50.18 | 54.15 |
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+ | **TRECCOVID** | **84.59** | 80.12 | 75.9 | 78.07 |
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+ | **ArguAna** | 50.09 | 57.59 | 59.55 | **63.61** |
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+ | **ClimateFEVER**| **33.07** | 35.2 | 31.84 | 31.17 |
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+ | **CQADupstackRetrieval** | 38.75 | 39.65 | 39.05 | **42.35** |
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+ | **DBPedia** | **45.58** | 41.02 | 40.03 | 40.77 |
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+ | **FEVER** | **90.96** | 87.13 | 86.64 | 86.29 |
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+ | **NFCorpus** | **37.3** | 34.92 | 34.3 | 37.39 |
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+ | **QuoraRetrieval** | 87.72 | 88.41 | **88.78** | 88.9 |
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+ | **SCIDOCS** | 18.42 | **21.82** | 20.52 | 21.73 |
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+ | **SciFact** | **74.77** | 72.22 | 71.28 | 74.04 |
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+ | **Touche2020** | 25.69 | 23.48 | **26.04** | 25.7 |
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+
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+ ### Against ColBERTv2.0
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+ | Dataset / Model | answerai-colbert-small-v1 | ColBERTv2.0 |
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+ |:-----------------|:-----------------------:|:------------:|
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+ | **BEIR AVG** | **53.79** | 50.02 |
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+ | **DBPedia** | **45.58** | 44.6 |
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+ | **FiQA2018** | **41.15** | 35.6 |
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+ | **NQ** | **59.1** | 56.2 |
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+ | **HotpotQA** | **76.11** | 66.7 |
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+ | **NFCorpus** | **37.3** | 33.8 |
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+ | **TRECCOVID** | **84.59** | 73.3 |
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+ | **Touche2020** | 25.69 | **26.3** |
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+ | **ArguAna** | **50.09** | 46.3 |
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+ | **ClimateFEVER**| **33.07** | 17.6 |
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+ | **FEVER** | **90.96** | 78.5 |
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+ | **QuoraRetrieval** | **87.72** | 85.2 |
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+ | **SCIDOCS** | **18.42** | 15.4 |
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+ | **SciFact** | **74.77** | 69.3 |