Rank1 7b

🚀 rank1-7b: Test-Time Compute for Reranking in Information Retrieval

rank1 is a reasoning reranker model that makes relevance judgments after "thinking". This 7B parameter model is trained from the Qwen2.5 - 7B base model. It uses test - time compute to generate reasoning chains before deciding if a document is relevant to a query, offering a new solution for information retrieval.

📄 Paper | 🚀 GitHub Repository

🚀 Quick Start

rank1 is a reasoning reranker model that "thinks" before making relevance judgments. This 7B parameter model is trained from the Qwen2.5 - 7B base model and leverages test - time compute to generate reasoning chains before deciding if a document is relevant to a query.

✨ Features

rank1 introduces a novel approach to information retrieval by generating explicit reasoning chains before making relevance judgments. Unlike traditional rerankers that directly output scores, rank1:

1. Receives a query and document pair
2. Generates a reasoning chain within a <think>...</think> section
3. Makes a binary relevance judgment (true or false)
4. Returns a confidence score based on the logits of the true/false tokens

This approach helps the model break down complex relevance decisions into logical steps, improving performance across diverse retrieval tasks.

📦 Installation

Please see the Github for detailed installation instructions.

💻 Usage Examples

Basic Usage

Note that official usage is found on the Github and accounts for edge cases. But for simple use cases the minimal example below works.

from vllm import LLM, SamplingParams
import math

# Initialize the model with vLLM
model = LLM(
    model="jhu-clsp/rank1-7b",
    tensor_parallel_size=1,  # Number of GPUs
    trust_remote_code=True,
    max_model_len=16000,     # Context length
    gpu_memory_utilization=0.9,
    dtype="float16",
)

# Set up sampling parameters
sampling_params = SamplingParams(
    temperature=0,
    max_tokens=8192,
    logprobs=20,
    stop=["</think> true", "</think> false"],
    skip_special_tokens=False
)

# Prepare the prompt
def create_prompt(query, document):
    return (
        "Determine if the following passage is relevant to the query. "
        "Answer only with 'true' or 'false'.\n"
        f"Query: {query}\n"
        f"Passage: {document}\n"
        "<think>"
    )

# Example usage
query = "What are the effects of climate change?"
document = "Climate change leads to rising sea levels, extreme weather events, and disruptions to ecosystems. These effects are caused by increasing greenhouse gas concentrations in the atmosphere due to human activities."

# Generate prediction
prompt = create_prompt(query, document)
outputs = model.generate([prompt], sampling_params)

# Extract score
output = outputs[0].outputs[0]
text = output.text
final_logits = output.logprobs[-1]

# Get token IDs for "true" and "false" tokens
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/rank1-7b")
true_token = tokenizer(" true", add_special_tokens=False).input_ids[0]
false_token = tokenizer(" false", add_special_tokens=False).input_ids[0]

# Calculate relevance score (probability of "true")
true_logit = final_logits[true_token].logprob
false_logit = final_logits[false_token].logprob
true_score = math.exp(true_logit)
false_score = math.exp(false_logit)
relevance_score = true_score / (true_score + false_score)

print(f"Reasoning chain: {text}")
print(f"Relevance score: {relevance_score}")

Advanced Usage

rank1 is compatible with the MTEB benchmarking framework:

from mteb import MTEB
from rank1 import rank1  # From the official repo

# Initialize the model
model = rank1(
    model_name_or_path="jhu-clsp/rank1-7b",
    num_gpus=1,
    device="cuda"
)

# Run evaluation on specific tasks
evaluation = MTEB(tasks=["NevIR"])
results = evaluation.run(model)

📚 Documentation

Model Family

Quantized Variants

Associated Data and Resources

Performance

rank1 - 7b demonstrates strong performance on retrieval benchmarks, particularly on tasks requiring complex reasoning. The model's ability to "think through" relevance decisions makes it especially effective for nuanced topics.

For specific benchmark results and comparisons with other models, please refer to the paper and the official GitHub repository.

📄 License

MIT License

📖 Citation

If you use rank1 in your research, please cite our work:

@misc{weller2025rank1testtimecomputereranking,
      title={Rank1: Test-Time Compute for Reranking in Information Retrieval}, 
      author={Orion Weller and Kathryn Ricci and Eugene Yang and Andrew Yates and Dawn Lawrie and Benjamin Van Durme},
      year={2025},
      eprint={2502.18418},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2502.18418}, 
}