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Model: jhu-clsp/rank1-0.5b Source: Original Platform
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README.md
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---
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base_model:
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- Qwen/Qwen2.5-0.5B
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datasets:
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- jhu-clsp/rank1-training-data
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language:
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- en
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license: mit
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pipeline_tag: text-ranking
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tags:
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- reranker
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- retrieval
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library_name: transformers
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---
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# rank1-0.5b: Test-Time Compute for Reranking in Information Retrieval
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📄 [Paper](https://arxiv.org/abs/2502.18418) | 🚀 [GitHub Repository](https://github.com/orionw/rank1)
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rank1 is a reasoning reranker model that "thinks" before making relevance judgments. This 0.5B parameter model is trained from the Qwen2.5-0.5B base model and leverages test-time compute to generate reasoning chains before deciding if a document is relevant to a query.
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## Model Description
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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:
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1. Receives a query and document pair
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2. Generates a reasoning chain within a `<think>...</think>` section
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3. Makes a binary relevance judgment (`true` or `false`)
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4. Returns a confidence score based on the logits of the true/false tokens
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This approach helps the model break down complex relevance decisions into logical steps, improving performance across diverse retrieval tasks.
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## Model Family
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| Model | Base | Description |
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|:------|:-----|:------------|
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| [rank1-0.5b](https://huggingface.co/jhu-clsp/rank1-0.5b) | Qwen2.5-0.5B | Current model (0.5B parameters) |
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| [rank1-1.5b](https://huggingface.co/jhu-clsp/rank1-1.5b) | Qwen2.5-1.5B | Larger variant (1.5B parameters) |
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| [rank1-3b](https://huggingface.co/jhu-clsp/rank1-3b) | Qwen2.5-3B | Larger variant (3B parameters) |
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| [rank1-7b](https://huggingface.co/jhu-clsp/rank1-7b) | Qwen2.5-7B | Larger variant (7B parameters) |
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| [rank1-14b](https://huggingface.co/jhu-clsp/rank1-14b) | Qwen2.5-14B | Larger variant (14B parameters) |
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| [rank1-32b](https://huggingface.co/jhu-clsp/rank1-32b) | Qwen2.532B | Largest variant (32B parameters) |
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| [rank1-mistral-2501-24b](https://huggingface.co/jhu-clsp/rank1-mistral-2501-24b) | Mistral-Small 2501 24B | Trained from Mistral base |
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| [rank1-llama3-8b](https://huggingface.co/jhu-clsp/rank1-llama3-8b) | Llama 3.1 8B | Trained from Llama 3.1 base |
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### Quantized Variants
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| Model | Description |
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|:------|:------------|
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| [rank1-7b-awq](https://huggingface.co/jhu-clsp/rank1-7b-awq) | Quantized version of rank1-7b |
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| [rank1-14b-awq](https://huggingface.co/jhu-clsp/rank1-14b-awq) | Quantized version of rank1-14b |
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| [rank1-32b-awq](https://huggingface.co/jhu-clsp/rank1-32b-awq) | Quantized version of rank1-32b |
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| [rank1-mistral-2501-24b-awq](https://huggingface.co/jhu-clsp/rank1-mistral-2501-24b-awq) | Quantized version of rank1-mistral-24b |
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| [rank1-llama3-8b-awq](https://huggingface.co/jhu-clsp/rank1-llama3-8b-awq) | Quantized version of rank1-llama3-8b |
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## Associated Data and Resources
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| Resource | Description |
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|:---------|:------------|
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| [rank1-r1-msmarco](https://huggingface.co/datasets/jhu-clsp/rank1-r1-msmarco) | All R1 output examples from MS MARCO |
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| [rank1-training-data](https://huggingface.co/datasets/jhu-clsp/rank1-training-data) | Training data used for rank1 models |
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| [rank1-run-files](https://huggingface.co/datasets/jhu-clsp/rank1-run-files) | Pre-computed run files for use in top 100 doc reranking |
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| [GitHub Repository](https://github.com/orionw/rank1) | Official rank1 repository |
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## Usage
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Note that official usage is found on the Github and accounts for edge cases. But for simple use cases the minimal example below works.
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<details>
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<summary>Click to expand: Minimal example with vLLM</summary>
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```python
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from vllm import LLM, SamplingParams
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import math
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# Initialize the model with vLLM
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model = LLM(
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model="jhu-clsp/rank1-0.5b",
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tensor_parallel_size=1, # Number of GPUs
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trust_remote_code=True,
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max_model_len=16000, # Context length
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gpu_memory_utilization=0.9,
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dtype="float16",
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)
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# Set up sampling parameters
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sampling_params = SamplingParams(
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temperature=0,
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max_tokens=8192,
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logprobs=20,
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stop=["</think> true", "</think> false"],
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skip_special_tokens=False
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)
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# Prepare the prompt
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def create_prompt(query, document):
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return (
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"Determine if the following passage is relevant to the query. "
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"Answer only with 'true' or 'false'.\n"
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f"Query: {query}\n"
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f"Passage: {document}\n"
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"<think>"
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)
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# Example usage
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query = "What are the effects of climate change?"
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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."
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# Generate prediction
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prompt = create_prompt(query, document)
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outputs = model.generate([prompt], sampling_params)
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# Extract score
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output = outputs[0].outputs[0]
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text = output.text
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final_logits = output.logprobs[-1]
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# Get token IDs for "true" and "false" tokens
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/rank1-0.5b")
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true_token = tokenizer(" true", add_special_tokens=False).input_ids[0]
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false_token = tokenizer(" false", add_special_tokens=False).input_ids[0]
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# Calculate relevance score (probability of "true")
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true_logit = final_logits[true_token].logprob
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false_logit = final_logits[false_token].logprob
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true_score = math.exp(true_logit)
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false_score = math.exp(false_logit)
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relevance_score = true_score / (true_score + false_score)
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print(f"Reasoning chain: {text}")
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print(f"Relevance score: {relevance_score}")
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```
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</details>
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## Performance
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rank1-0.5b 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.
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For specific benchmark results and comparisons with other models, please refer to the paper and the official GitHub repository.
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## Installation
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Please see the Github for detailed installation instructions.
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## MTEB Integration
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rank1 is compatible with the [MTEB benchmarking framework](https://github.com/embeddings-benchmark/mteb):
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```python
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from mteb import MTEB
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from rank1 import rank1 # From the official repo
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# Initialize the model
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model = rank1(
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model_name_or_path="jhu-clsp/rank1-0.5b",
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num_gpus=1,
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device="cuda"
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)
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# Run evaluation on specific tasks
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evaluation = MTEB(tasks=["NevIR"])
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results = evaluation.run(model)
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```
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## Citation
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If you use rank1 in your research, please cite our work:
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```bibtex
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@misc{weller2025rank1testtimecomputereranking,
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title={Rank1: Test-Time Compute for Reranking in Information Retrieval},
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author={Orion Weller and Kathryn Ricci and Eugene Yang and Andrew Yates and Dawn Lawrie and Benjamin Van Durme},
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year={2025},
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eprint={2502.18418},
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archivePrefix={arXiv},
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primaryClass={cs.IR},
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url={https://arxiv.org/abs/2502.18418},
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}
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```
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## License
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[MIT License](https://github.com/orionw/rank1/blob/main/LICENSE)
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config.json
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},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151666": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>",
|
||||
"<think>",
|
||||
"</think>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user