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Full List of Models Available for Download

BiEncoder Retrievers

Model Training data Architecture Embedding Dim NQ EM + rerank TQA EM + rerank Download Resource Key Name
retriever_multi_base_256 (recommended) NQ + TQA AlBERT-base 256 41.4 47.3 40.2 50.9 models.retrievers.retriever_multi_base_256
retriever_multi_base NQ + TQA AlBERT-base 728 40.9 47.4 39.7 51.2 models.retrievers.retriever_multi_base
retriever_multi_large NQ + TQA AlBERT-large 728 41.2 47.5 41.0 51.9 models.retrievers.retriever_multi_large
retriever_multi_xlarge NQ + TQA AlBERT-xlarge 728 41.7 47.6 41.3 52.1 models.retrievers.retriever_multi_xlarge
retriever_nq_base NQ AlBERT-base 728 41.0 47.2 35.6 49.0 models.retrievers.retriever_nq_base
retriever_nq_large NQ AlBERT-large 728 40.4 47.3 34.1 48.1 models.retrievers.retriever_nq_large
retriever_nq_xlarge NQ AlBERT-xlarge 728 41.1 47.7 35.7 48.9 models.retrievers.retriever_nq_xlarge
retriever_tqa_base TQA AlBERT-base 728 37.5 46.8 38.7 51.0 models.retrievers.retriever_tqa_base
retriever_tqa_large TQA AlBERT-large 728 38.2 47.0 39.6 51.4 models.retrievers.retriever_tqa_large
retriever_tqa_xlarge TQA AlBERT-xlarge 728 38.0 46.5 38.9 51.2 models.retrievers.retriever_tqa_xlarge

(Rerank scores calculated with reranker_multi_xxlarge)

QA Rerankers

Model Training data Architecture NQ EM TQA EM Download Resource Key Name
reranker_multi_base NQ + TQA AlBERT-base 46.0 48.9 models.rerankers.reranker_multi_base
reranker_multi_large NQ + TQA AlBERT-large 46.2 49.4 models.rerankers.reranker_multi_large
reranker_multi_xlarge NQ + TQA AlBERT-xlarge 46.0 49.1 models.rerankers.reranker_multi_xlarge
reranker_multi_xxlarge NQ + TQA AlBERT-xxlarge 47.7 52.1 models.rerankers.reranker_multi_xxlarge
reranker_nq_xlarge NQ AlBERT-xlarge 45.2 46.7 models.rerankers.reranker_nq_xlarge
reranker_nq_xxlarge NQ AlBERT-xxlarge 46.4 49.6 models.rerankers.reranker_nq_xxlarge
reranker_tqa_xlarge TQA AlBERT-xlarge 45.0 49.7 models.rerankers.reranker_tqa_xlarge
reranker_tqa_xxlarge TQA AlBERT-xxlarge 46.0 51.7 models.rerankers.reranker_tqa_xxlarge

(EM scores in this table calculated using retriever_multi_xlarge retriever)

Qgen Models

Model Training data Architecture Download Resource Key Name
qgen_nq_base NQ BART-base models.qgen.qgen_nq_base
qgen_multi_base Multitask BART-base models.qgen.qgen_multi_base

Passage Ranker Models

Models used for selecting passages to generate questions from:

Model Training data Architecture Download Resource Key Name
passage_ranker_base NQ BERT-base models.passage_rankers.passage_ranker_base

Note, the original Passage ranker model used in the paper was unfortunately lost due to a storage corruption issue. The model here is a reproduction using the same hardware and HPs, but differs a little due to the stochastic training sampling procedure.

Answer Extractor Models

Model Description Training data Architecture Download Resource Key Name
answer_extractor_nq_base Learnt Answer Span Extractor, BERT-base, NQ-trained NQ BERT-base models.answer_extractors.answer_extractor_nq_base

Filterer Models

Model Description Training data Architecture Download Resource Key Name
dpr_nq_passage_retriever DPR Passage retriever and faiss index, from the DPR Paper, used for retrieving passage for the reader in global filtering, NQ-trained NQ BERT-base models.filtering.dpr_nq_passage_retriever
fid_reader_nq_base FID-base reader, from the Fusion-in-Decoder paper, used in global and local filtering, NQ-trained NQ t5-base models.filtering.fid_reader_nq_base