DistilBert for Dense Passage Retrieval trained with Balanced Topic Aware Sampling (TAS-B)

We provide a retrieval trained DistilBert-based model (we call the dual-encoder then dot-product scoring architecture BERT_Dot) trained with Balanced Topic Aware Sampling on ms_marco人工智能ai哪个好-Passage.

This instance was trained with a batch size of 256 and can be used to re-rank a candidate set制作ai的软件 or directly for a vector index based dense retrieval百度ai智能云. The architecture is a 6-layer DistilBERT, without architecture additions or modifications (we only change the weights during training) – to receive a query/passage representation we pool the CLS vector. We use the same BERT layers for both query and passage encoding (yields better results, and lowers memory requirements).

If you want to know more about our efficient (can be done on a single consumer GPU in 48 hours) batch composition procedure and dual supervision for dense retrieval training, check out our paper: https://arxiv.org/abs/2104.06967 ?元宝大模型

For more information and a minimal usage example please visit: https://github.com/sebastian-hofstaetter/tas-balanced-dense-retrieval下载官方即梦a1

Effectiveness on MSMARCO Passage & TREC-DL’19

We trained our model on the MSMARCO standard (“small”-400K query) training triples re-sampled with our TAS-B method. As teacher models we used the BERT_CAT pairwise scores as well as the ColBERT model for in-batch-negative signals published here: https://github.com/sebastian-hofstaetter/neural-ranking-kd百度ai智能云

MSMARCO-DEV (7K)

TREC-DL’19

For MRR and Recall we use the recommended binarization point of the graded relevance of 2. This might skew the results when compared to other binarization point numbers.猫箱下载安装

TREC-DL’20

For MRR and Recall we use the recommended binarization point of the graded relevance of 2. This might skew the results when compared to other binarization point numbers.下载官方即梦a1

For more baselines, info and analysis, please see the paper: https://arxiv.org/abs/2104.06967人工智能ai哪个好

Limitations & Bias

• The model inherits social biases from both DistilBERT and MSMARCO.ai分析软件

• The model is only trained on relatively short passages of MSMARCO (avg. 60 words length), so it might struggle with longer text.百度aiapp

Citation

If you use our model checkpoint please cite our work as:有戏ai

@inproceedings{Hofstaetter2021_tasb_dense_retrieval,
 author = {Sebastian Hofst{\"a}tter and Sheng-Chieh Lin and Jheng-Hong Yang and Jimmy Lin and Allan Hanbury},
 title = {{Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling}},
 booktitle = {Proc. of SIGIR},
 year = {2021},
}