inf-retriever-v1-pro

📖 Overview

inf-retriever-v1-pro is a specialized retrieval component of the INF-X-Retriever framework, designed to distill the core retrieval intent from complex, verbose, or reasoning-intensive queries. Built upon the inf-retriever-v1 foundation and further trained to serve as the retriever within a RAG (retrieval-augmented generation) system, it transforms raw user queries into concise, search-optimized queries for dense retrieval systems.

In our experiments, a single canonical query-writing prompt was applied across all datasets to ensure consistency and reproducibility.

task = 'Given a web search query, retrieve relevant passages that answer the query'

This model is a key enabler for INF-X-Retriever's state-of-the-art performance, currently holding the No. 1 position on the BRIGHT Benchmark (as of Dec 17, 2025).

For more details on the full framework, please visit the INF-X-Retriever Repository.

Requirements

transformers==4.51.0

Usage

Sentence Transformers

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("infly/inf-retriever-v1", trust_remote_code=True)
# In case you want to reduce the maximum length:
model.max_seq_length = 8192

queries = [
    "how much protein should a female eat",
    "summit define",
]
documents = [
    "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
    "Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments.",
]

query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)

scores = (query_embeddings @ document_embeddings.T) * 100
print(scores.tolist())
# [[91.46116638183594, 76.9832992553711], [70.7034683227539, 87.15817260742188]]

Transformers

import torch
import torch.nn.functional as F

from torch import Tensor
from transformers import AutoTokenizer, AutoModel


def last_token_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
    if left_padding:
        return last_hidden_states[:, -1]
    else:
        sequence_lengths = attention_mask.sum(dim=1) - 1
        batch_size = last_hidden_states.shape[0]
        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]


def get_detailed_instruct(task_description: str, query: str) -> str:
    return f'Instruct: {task_description}\nQuery: {query}'


# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
    get_detailed_instruct(task, 'how much protein should a female eat'),
    get_detailed_instruct(task, 'summit define')
]
# No need to add instruction for retrieval documents
documents = [
    "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
    "Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments."
]
input_texts = queries + documents

tokenizer = AutoTokenizer.from_pretrained('infly/inf-retriever-v1', trust_remote_code=True)
model = AutoModel.from_pretrained('infly/inf-retriever-v1', trust_remote_code=True)

max_length = 8192

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
# [[91.46114349365234, 76.98332214355469], [70.7035140991211, 87.158203125]]

Performance

INF-X-Retriever achieves state-of-the-art results on the BRIGHT Benchmark (as of Dec 20, 2025).

The BRIGHT (Benchmark for Reasoning-Intensive Grounded HT) is a rigorous text retrieval benchmark designed to evaluate the capability of retrieval models in handling questions that require intensive reasoning and cross-document synthesis. Collected from real-world sources such as StackExchange, competitive programming platforms, and mathematical competitions, it comprises complex queries spanning diverse domains like mathematics, coding, biology, economics, and robotics.

Short document

Overall & Category Performance

Model	Avg ALL	StackExchange	Coding	Theorem-based
INF-X-Retriever	63.4	68.3	55.3	57.7
DIVER (v3)	46.8	51.8	39.9	39.7
BGE-Reasoner-0928	46.4	52.0	35.3	40.7
LATTICE	42.1	51.6	26.9	30.0
ReasonRank	40.8	46.9	27.6	35.5
XDR2	40.3	47.1	28.5	32.1

Detailed Results Across 12 Datasets

Model	Avg	Bio.	Earth.	Econ.	Psy.	Rob.	Stack.	Sus.	Leet.	Pony	AoPS	TheoQ.	TheoT.
INF-X-Retriever	63.4	79.8	70.9	69.9	73.3	57.7	64.3	61.9	56.1	54.5	51.9	53.1	67.9
DIVER (v3)	46.8	66.0	63.7	42.4	55.0	40.6	44.7	50.4	32.5	47.3	17.2	46.4	55.6
BGE-Reasoner-0928	46.4	68.5	66.4	40.6	53.1	43.2	44.1	47.8	29.0	41.6	17.2	46.5	58.4
LATTICE	42.1	64.4	62.4	45.4	57.4	47.6	37.6	46.4	19.9	34.0	12.0	30.1	47.8
ReasonRank	40.8	62.7	55.5	36.7	54.6	35.7	38.0	44.8	29.5	25.6	14.4	42.0	50.1
XDR2	40.3	63.1	55.4	38.5	52.9	37.1	38.2	44.6	21.9	35.0	15.7	34.4	46.2

Long document

Detailed Results Across 8 Datasets

Model	Avg	Bio.	Earth.	Econ.	Pony	Psy.	Rob.	Stack.	Sus.
INF-X-Retriever	54.6	73.2	59.6	69.3	12.1	74.3	55.9	27.8	64.8
inf-retriever-v1-pro	30.5	44.1	42.2	31.4	0.4	43.1	20.8	21.4	41.0

🖊️ Citation

If you find this model useful, please consider citing our work:

@misc{inf-x-retriever-2025,
    title        = {INF-X-Retriever},
    author       = {Yichen Yao, Jiahe Wan, Yuxin Hong, Mengna Zhang, Junhan Yang, Zhouyu Jiang, Qing Xu, Kuan Lu, Yinghui Xu, Wei Chu, Emma Wang, Yuan Qi},
    year         = {2025},
    url          = {https://yaoyichen.github.io/INF-X-Retriever},
    publisher    = {GitHub repository}
}