---
title: spar-paq-bm25-lexmodel-context-encoder
canonical_url: "https://www.modelscope.cn/models/facebook/spar-paq-bm25-lexmodel-context-encoder"
md_url: "https://www.modelscope.cn/models/facebook/spar-paq-bm25-lexmodel-context-encoder.md"
repository: facebook/spar-paq-bm25-lexmodel-context-encoder
last_updated: 2025-05-20
pipeline_tag: feature-extraction
tasks:
  - feature-extraction
model_type:
  - bert
architectures:
  - BertModel
library_name:
  - pytorch
  - transformer
frameworks:
  - pytorch
inference_backends:
  - "deploy_task emb"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 331
stars: 0
tags:
  - feature-extraction
---

# spar-paq-bm25-lexmodel-context-encoder

> spar-paq-bm25-lexmodel-context-encoder - facebook 在 ModelScope 开源的模型。This model is the context encoder of the PAQ BM25 Lexical Model (Λ) from the SPAR paper:

facebook/spar-paq-bm25-lexmodel-context-encoder 是 ModelScope 魔搭社区上的feature-extraction模型，可用 deploy_task emb、sglang 0.5.2、vllm 0.9.2 部署。

- **Repository**: facebook/spar-paq-bm25-lexmodel-context-encoder
- **Tasks**: feature-extraction
- **Inference backends**: deploy_task emb, sglang 0.5.2, vllm 0.9.2
- **Tags**: feature-extraction
- **Downloads**: 331
- **Stars**: 0
- **Last updated**: 2025-05-20

Source: https://www.modelscope.cn/models/facebook/spar-paq-bm25-lexmodel-context-encoder

---

This model is the context encoder of the PAQ BM25 Lexical Model (Λ) from the SPAR paper:

[Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?](https://arxiv.org/abs/2110.06918)
<br>
Xilun Chen, Kushal Lakhotia, Barlas Oğuz, Anchit Gupta, Patrick Lewis, Stan Peshterliev, Yashar Mehdad, Sonal Gupta and Wen-tau Yih
<br>
**Meta AI**

The associated github repo is available here: https://github.com/facebookresearch/dpr-scale/tree/main/spar

This model is a BERT-base sized dense retriever trained using PAQ questions as queries to imitate the behavior of BM25.
The following models are also available:
Pretrained Model | Corpus | Teacher | Architecture | Query Encoder Path | Context Encoder Path
|---|---|---|---|---|---
Wiki BM25 Λ | Wikipedia | BM25 | BERT-base | facebook/spar-wiki-bm25-lexmodel-query-encoder | facebook/spar-wiki-bm25-lexmodel-context-encoder
PAQ BM25 Λ | PAQ | BM25 | BERT-base | facebook/spar-paq-bm25-lexmodel-query-encoder | facebook/spar-paq-bm25-lexmodel-context-encoder
MARCO BM25 Λ | MS MARCO | BM25 | BERT-base | facebook/spar-marco-bm25-lexmodel-query-encoder | facebook/spar-marco-bm25-lexmodel-context-encoder
MARCO UniCOIL Λ | MS MARCO | UniCOIL | BERT-base | facebook/spar-marco-unicoil-lexmodel-query-encoder | facebook/spar-marco-unicoil-lexmodel-context-encoder

# Using the Lexical Model (Λ) Alone

This model should be used together with the associated query encoder, similar to the [DPR](https://huggingface.co/docs/transformers/v4.22.1/en/model_doc/dpr) model.

```
import torch
from transformers import AutoTokenizer, AutoModel

# The tokenizer is the same for the query and context encoder
tokenizer = AutoTokenizer.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder')
query_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder')
context_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-context-encoder')

query = "Where was Marie Curie born?"
contexts = [
    "Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.",
    "Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace."
]

# Apply tokenizer
query_input = tokenizer(query, return_tensors='pt')
ctx_input = tokenizer(contexts, padding=True, truncation=True, return_tensors='pt')

# Compute embeddings: take the last-layer hidden state of the [CLS] token
query_emb = query_encoder(**query_input).last_hidden_state[:, 0, :]
ctx_emb = context_encoder(**ctx_input).last_hidden_state[:, 0, :]

# Compute similarity scores using dot product
score1 = query_emb @ ctx_emb[0]  # 341.3268
score2 = query_emb @ ctx_emb[1]  # 340.1626

```

# Using the Lexical Model (Λ) with a Base Dense Retriever as in SPAR
As Λ learns lexical matching from a sparse teacher retriever, it can be used in combination with a standard dense retriever (e.g. [DPR](https://huggingface.co/docs/transformers/v4.22.1/en/model_doc/dpr#dpr), [Contriever](https://huggingface.co/facebook/contriever-msmarco)) to build a dense retriever that excels at both lexical and semantic matching.

In the following example, we show how to build the SPAR-Wiki model for Open-Domain Question Answering by concatenating the embeddings of DPR and the Wiki BM25 Λ.
```
import torch
from transformers import AutoTokenizer, AutoModel
from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer
from transformers import DPRContextEncoder, DPRContextEncoderTokenizer

# DPR model
dpr_ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-multiset-base")
dpr_ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-multiset-base")
dpr_query_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained("facebook/dpr-question_encoder-multiset-base")
dpr_query_encoder = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-multiset-base")

# Wiki BM25 Λ model
lexmodel_tokenizer = AutoTokenizer.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder')
lexmodel_query_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder')
lexmodel_context_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-context-encoder')

query = "Where was Marie Curie born?"
contexts = [
    "Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.",
    "Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace."
]

# Compute DPR embeddings
dpr_query_input = dpr_query_tokenizer(query, return_tensors='pt')['input_ids']
dpr_query_emb = dpr_query_encoder(dpr_query_input).pooler_output
dpr_ctx_input = dpr_ctx_tokenizer(contexts, padding=True, truncation=True, return_tensors='pt')
dpr_ctx_emb = dpr_ctx_encoder(**dpr_ctx_input).pooler_output

# Compute Λ embeddings
lexmodel_query_input = lexmodel_tokenizer(query, return_tensors='pt')
lexmodel_query_emb = lexmodel_query_encoder(**query_input).last_hidden_state[:, 0, :]
lexmodel_ctx_input = lexmodel_tokenizer(contexts, padding=True, truncation=True, return_tensors='pt')
lexmodel_ctx_emb = lexmodel_context_encoder(**ctx_input).last_hidden_state[:, 0, :]

# Form SPAR embeddings via concatenation

# The concatenation weight is only applied to query embeddings
# Refer to the SPAR paper for details
concat_weight = 0.7

spar_query_emb = torch.cat(
    [dpr_query_emb, concat_weight * lexmodel_query_emb],
    dim=-1,
    )
spar_ctx_emb = torch.cat(
    [dpr_ctx_emb, lexmodel_ctx_emb],
    dim=-1,
)

# Compute similarity scores
score1 = spar_query_emb @ spar_ctx_emb[0]  # 317.6931
score2 = spar_query_emb @ spar_ctx_emb[1]  # 314.6144
```