| Aggretriever is an encoder to aggregate both lexical and semantic text information into a single-vector dense vector for dense retrieval, which is finetued on MS MARCO corpus with BM25 negative sampling, following the approach described in [Aggretriever: A Simple Approach to Aggregate Textual Representation for Robust Dense Passage Retrieval](https://arxiv.org/abs/2208.00511). | |
| <p align="center"> | |
| <img src="https://raw.githubusercontent.com/castorini/dhr/main/fig/aggretriever_teaser.png" width="600"> | |
| </p> | |
| The associated GitHub repository for fine-tuning is available [here](https://github.com/castorini/dhr) and the reproduce from pyserini is [here]. The following variants are also available: | |
| Model | Initialization | MARCO Dev | Encoder Path | |
| |---|---|---|--- | |
| aggretriever-distilbert | distilbert-base-uncased | 34.1 | [castorini/aggretriever-distilbert](https://huggingface.co/castorini/aggretriever-distilbert) | |
| aggretriever-cocondenser | Luyu/co-condenser-marco | 36.2 | [castorini/aggretriever-cocondenser](https://huggingface.co/castorini/aggretriever-cocondenser) | |
| ## Usage (HuggingFace Transformers) | |
| Using the model directly available in HuggingFace transformers. We use the implemented Aggretriever from pyserini [here](https://github.com/castorini/pyserini/blob/master/pyserini/encode/_aggretriever.py). | |
| ```python | |
| from pyserini.encode._aggretriever import AggretrieverQueryEncoder | |
| from pyserini.encode._aggretriever import AggretrieverDocumentEncoder | |
| model_name = '/store/scratch/s269lin/experiments/aggretriever/hf_model/aggretriever-cocondenser' | |
| query_encoder = AggretrieverQueryEncoder(model_name, device='cpu') | |
| context_encoder = AggretrieverDocumentEncoder(model_name, device='cpu') | |
| query = ["Where was Marie Curie born?"] | |
| contexts = [ | |
| "Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.", | |
| "Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace." | |
| ] | |
| # Compute embeddings: take the last-layer hidden state of the [CLS] token | |
| query_emb = query_encoder.encode(query) | |
| ctx_emb = context_encoder.encode(contexts) | |
| # Compute similarity scores using dot product | |
| score1 = query_emb @ ctx_emb[0] # 45.56658 | |
| score2 = query_emb @ ctx_emb[1] # 45.81762 | |
| ``` |