intfloat/simlm-base-msmarco-finetuned

SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval

paper available at https://arxiv.org/pdf/2207.02578

code available at https://github.com/microsoft/unilm/tree/master/simlm

Paper abstract

In this paper, we propose SimLM (Similarity matching with Language Model pre-training), a simple yet effective pre-training method for dense passage retrieval. It employs a simple bottleneck architecture that learns to compress the passage information into a dense vector through self-supervised pre-training. We use a replaced language modeling objective, which is inspired by ELECTRA, to improve the sample efficiency and reduce the mismatch of the input distribution between pre-training and fine-tuning. SimLM only requires access to unlabeled corpus, and is more broadly applicable when there are no labeled data or queries. We conduct experiments on several large-scale passage retrieval datasets, and show substantial improvements over strong baselines under various settings. Remarkably, SimLM even outperforms multi-vector approaches such as ColBERTv2 which incurs significantly more storage cost.

Results on MS-MARCO passage ranking task

Model	dev MRR@10	dev R@50	dev R@1k	TREC DL 2019 nDCG@10	TREC DL 2020 nDCG@10
RocketQAv2	38.8	86.2	98.1	-	-
coCondenser	38.2	86.5	98.4	71.7	68.4
ColBERTv2	39.7	86.8	98.4	-	-
SimLM (this model)	41.1	87.8	98.7	71.4	69.7

Usage

Get embeddings from our fine-tuned model:

import torch
from transformers import AutoModel, AutoTokenizer, BatchEncoding, PreTrainedTokenizerFast
from transformers.modeling_outputs import BaseModelOutput

def l2_normalize(x: torch.Tensor):
    return torch.nn.functional.normalize(x, p=2, dim=-1)

def encode_query(tokenizer: PreTrainedTokenizerFast, query: str) -> BatchEncoding:
    return tokenizer(query,
                     max_length=32,
                     padding=True,
                     truncation=True,
                     return_tensors='pt')

def encode_passage(tokenizer: PreTrainedTokenizerFast, passage: str, title: str = '-') -> BatchEncoding:
    return tokenizer(title,
                     text_pair=passage,
                     max_length=144,
                     padding=True,
                     truncation=True,
                     return_tensors='pt')

tokenizer = AutoTokenizer.from_pretrained('intfloat/simlm-base-msmarco-finetuned')
model = AutoModel.from_pretrained('intfloat/simlm-base-msmarco-finetuned')
model.eval()

with torch.no_grad():
    query_batch_dict = encode_query(tokenizer, 'what is qa')
    outputs: BaseModelOutput = model(**query_batch_dict, return_dict=True)
    query_embedding = l2_normalize(outputs.last_hidden_state[0, 0, :])

    psg1 = 'Quality assurance (QA) is a process-centered approach to ensuring that a company or organization is providing the best possible products or services. It is related to quality control, which focuses on the end result, such as testing a sample of items from a batch after production.'
    psg1_batch_dict = encode_passage(tokenizer, psg1)
    outputs: BaseModelOutput = model(**psg1_batch_dict, return_dict=True)
    psg1_embedding = l2_normalize(outputs.last_hidden_state[0, 0, :])

    psg2 = 'The Super Bowl is typically four hours long. The game itself takes about three and a half hours, with a 30 minute halftime show built in.'
    psg2_batch_dict = encode_passage(tokenizer, psg2)
    outputs: BaseModelOutput = model(**psg2_batch_dict, return_dict=True)
    psg2_embedding = l2_normalize(outputs.last_hidden_state[0, 0, :])

    # Higher cosine similarity means they are more relevant
    print(query_embedding.dot(psg1_embedding), query_embedding.dot(psg2_embedding))

Citation

@article{Wang2022SimLMPW,
  title={SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval},
  author={Liang Wang and Nan Yang and Xiaolong Huang and Binxing Jiao and Linjun Yang and Daxin Jiang and Rangan Majumder and Furu Wei},
  journal={ArXiv},
  year={2022},
  volume={abs/2207.02578}
}