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---
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language: "en"
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tags:
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- re-ranking
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- passage-ranking
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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-Cat (vanilla/mono/concatenated DistilBERT re-ranker)
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We provide a retrieval trained DistilBERT-Cat model. 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**. The architecure is a 6-layer DistilBERT, with an additional single linear layer at the end.
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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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## Configuration
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- fp16 trained, so fp16 inference shouldn't be a problem
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## Model Code
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````python
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from transformers import AutoTokenizer,AutoModel, PreTrainedModel,PretrainedConfig
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from typing import Dict
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import torch
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class BERT_Cat_Config(PretrainedConfig):
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model_type = "BERT_Cat"
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bert_model: str
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trainable: bool = True
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class BERT_Cat(PreTrainedModel):
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"""
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The vanilla/mono BERT concatenated (we lovingly refer to as BERT_Cat) architecture
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-> requires input concatenation before model, so that batched input is possible
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"""
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config_class = BERT_Cat_Config
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base_model_prefix = "bert_model"
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def __init__(self,
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cfg) -> None:
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super().__init__(cfg)
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self.bert_model = AutoModel.from_pretrained(cfg.bert_model)
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for p in self.bert_model.parameters():
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p.requires_grad = cfg.trainable
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self._classification_layer = torch.nn.Linear(self.bert_model.config.hidden_size, 1)
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def forward(self,
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query_n_doc_sequence):
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vecs = self.bert_model(**query_n_doc_sequence)[0][:,0,:] # assuming a distilbert model here
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score = self._classification_layer(vecs)
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return score
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tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") # honestly not sure if that is the best way to go, but it works :)
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model = BERT_Cat.from_pretrained("sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco")
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````
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## Effectiveness on MSMARCO Passage & TREC Deep Learning '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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Here, we use the larger 49K query DEV set (same range as the smaller 7K DEV set, minimal changes possible)
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| | MRR@10 | NDCG@10 |
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|----------------------------------|--------|---------|
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| BM25 | .194 | .241 |
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| **Margin-MSE DistilBERT_Cat** (Re-ranking) | .391 | .451 |
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### TREC-DL'19
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For MRR 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 |
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|----------------------------------|--------|---------|
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| BM25 | .689 | .501 |
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| **Margin-MSE DistilBERT_Cat** (Re-ranking) | .891 | .747 |
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For more metrics, 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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``` |
