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---
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language: "en"
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tags:
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- dpr
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- dense-passage-retrieval
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- knowledge-distillation
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datasets:
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- ms_marco
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---
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# Margin-MSE Trained DistilBert for Dense Passage Retrieval
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We provide a retrieval trained DistilBert-based model (we call the architecture BERT_Dot). Our model is trained with Margin-MSE using a 3 teacher BERT_Cat (concatenated BERT scoring) ensemble on MSMARCO-Passage.
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This instance can be used to **re-rank a candidate set** or **directly for a vector index based dense retrieval**. 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).
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If you want to know more about our simple, yet effective knowledge distillation method for efficient information retrieval models for a variety of student architectures that is used for this model instance check out our paper: https://arxiv.org/abs/2010.02666 🎉
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For more information, training data, source code, and a minimal usage example please visit: https://github.com/sebastian-hofstaetter/neural-ranking-kd
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## Effectiveness on MSMARCO Passage & TREC-DL'19
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We trained our model on the MSMARCO standard ("small"-400K query) training triples with knowledge distillation with a batch size of 32 on a single consumer-grade GPU (11GB memory).
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For re-ranking we used the top-1000 BM25 results.
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### MSMARCO-DEV
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| | MRR@10 | NDCG@10 | Recall@1K |
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|----------------------------------|--------|---------|-----------------------------|
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| BM25 | .194 | .241 | .868 |
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| **Margin-MSE BERT_Dot** (Re-ranking) | .332 | .391 | .868 (from BM25 candidates) |
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| **Margin-MSE BERT_Dot** (Retrieval) | .323 | .381 | .957 |
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### TREC-DL'19
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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.
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| | MRR@10 | NDCG@10 | Recall@1K |
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|----------------------------------|--------|---------|-----------------------------|
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| BM25 | .689 | .501 | .739 |
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| **Margin-MSE BERT_Dot** (Re-ranking) | .862 | .712 | .739 (from BM25 candidates) |
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| **Margin-MSE BERT_Dot** (Retrieval) | .868 | .697 | .769 |
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For more baselines, info and analysis, please see the paper: https://arxiv.org/abs/2010.02666
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## Limitations & Bias
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- The model inherits social biases from both DistilBERT and MSMARCO.
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- The model is only trained on relatively short passages of MSMARCO (avg. 60 words length), so it might struggle with longer text.
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## Citation
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If you use our model checkpoint please cite our work as:
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```
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@misc{hofstaetter2020_crossarchitecture_kd,
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title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation},
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author={Sebastian Hofst{\"a}tter and Sophia Althammer and Michael Schr{\"o}der and Mete Sertkan and Allan Hanbury},
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year={2020},
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eprint={2010.02666},
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archivePrefix={arXiv},
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primaryClass={cs.IR}
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}
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``` |
