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---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-4B
pipeline_tag: text-ranking
tags:
- finance
- legal
- code
- stem
- medical
library_name: sentence-transformers
model_max_length: 32768
---
<img src="https://i.imgur.com/oxvhvQu.png"/>
# Releasing zeroentropy/zerank-2
In search engines, [rerankers are crucial](https://www.zeroentropy.dev/blog/what-is-a-reranker-and-do-i-need-one) for improving the accuracy of your retrieval system.
However, SOTA rerankers are closed-source and proprietary. At ZeroEntropy, we've trained a SOTA reranker outperforming closed-source competitors, and we're launching our model here on HuggingFace.
This reranker [outperforms proprietary rerankers](https://www.zeroentropy.dev/articles/zerank-2-advanced-instruction-following-multimodal-reranker) such as `cohere-rerank-v3.5` and `gemini-2.5-flash` across a wide variety of domains, including finance, legal, code, STEM, medical, and conversational data.
At ZeroEntropy we've developed an innovative multi-stage pipeline that models query-document relevance scores as adjusted [Elo ratings](https://en.wikipedia.org/wiki/Elo_rating_system). See our Technical Report (https://arxiv.org/abs/2509.12541
) for more details.
This model is released under the [Apache License 2.0](./LICENSE).
## Model Details
| Property | Value |
|---|---|
| Parameters | 4B |
| Context Length | 32,768 tokens (32k) |
| Base Model | Qwen/Qwen3-4B |
| License | Apache-2.0 |
## How to Use
> **Breaking change (May 2026):** `model.predict()` now returns raw "Yes" logits instead of sigmoid'd probabilities in `[0, 1]`. Rankings are unchanged. To recover the previous 0-1 score, apply `(scores / 5).sigmoid()` — see the example below. Loading no longer requires `trust_remote_code=True`; passing it is harmless.
### Using Sentence Transformers
Install Sentence Transformers:
```bash
pip install sentence_transformers
```
Then load the model and score query/document pairs. `model.predict` returns the raw "Yes" logit per pair; rankings can be used directly. To map the logits to a 0-1 score range, apply a temperature-scaled sigmoid: `sigmoid(score / 5)`.
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder("zeroentropy/zerank-2")
query_documents = [
("What is 2+2?", "4"),
("What is 2+2?", "The answer is definitely 1 million"),
]
scores = model.predict(query_documents, convert_to_tensor=True)
print(scores)
# tensor([ 5.4062, -4.5000], device='cuda:0', dtype=torch.bfloat16)
# Optional: convert to 0-1 probabilities
probabilities = (scores / 5).sigmoid()
print(probabilities)
# tensor([0.7461, 0.2891], device='cuda:0', dtype=torch.bfloat16)
```
You can also use `model.rank` to score and sort a list of documents for a single query:
```python
rankings = model.rank(
"What is 2+2?",
["4", "The answer is definitely 1 million"],
)
for r in rankings:
print(r)
# {'corpus_id': 0, 'score': np.float32(5.40625)}
# {'corpus_id': 1, 'score': np.float32(-4.5)}
```
The model can also be inferenced using ZeroEntropy's [/models/rerank](https://docs.zeroentropy.dev/api-reference/models/rerank) endpoint, and on [AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-o7avk66msiukc).
## Evaluations
NDCG@10 scores between `zerank-2` and competing closed-source proprietary rerankers. Since we are evaluating rerankers, OpenAI's `text-embedding-3-small` is used as an initial retriever for the Top 100 candidate documents.
| Domain | OpenAI embeddings | ZeroEntropy zerank-2 | ZeroEntropy zerank-1 | Gemini 2.5 Flash (Listwise) | Cohere rerank-3.5 |
|------------------|-------------------|----------------------|----------------------|-----------------------------|-------------------|
| Web | 0.3819 | **0.6346** | 0.6069 | 0.5765 | 0.5594 |
| Conversational | 0.4305 | **0.6140** | 0.5801 | 0.6021 | 0.5648 |
| STEM & Logic | 0.3744 | **0.6521** | 0.6283 | 0.5447 | 0.5418 |
| Code | 0.4582 | **0.6528** | 0.6310 | 0.6128 | 0.5364 |
| Legal | 0.4101 | **0.6644** | 0.6222 | 0.5565 | 0.5257 |
| Biomedical | 0.4783 | **0.7217** | 0.6967 | 0.5371 | 0.6246 |
| Finance | 0.6232 | 0.7600 | 0.7539 | **0.7694** | 0.7402 |
| **Average** | **0.4509** | **0.6714** | **0.6456** | **0.5999** | **0.5847** |
<img src="https://cdn-uploads.huggingface.co/production/uploads/65ec60ccfc59f6e77ecc9ccb/UiDp8LsY4XIdRK5i3CAdD.png" alt="Graph showing the same table" width="1000"/>
## License
This model is licensed under the [Apache License 2.0](./LICENSE).

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{%- set zerank_query = messages | selectattr("role", "eq", "query") | map(attribute="content") | list -%}
{%- set zerank_document = messages | selectattr("role", "eq", "document") | map(attribute="content") | list -%}
{%- if zerank_query and zerank_document -%}
{{- '<|im_start|>system\n' + (zerank_query | first) + '<|im_end|>\n' -}}
{{- '<|im_start|>user\n' + (zerank_document | first) + '<|im_end|>\n' -}}
{%- if add_generation_prompt -%}
{{- '<|im_start|>assistant\n' -}}
{%- endif -%}
{%- else -%}
{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# 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>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\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" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}
{%- endif -%}

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"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 151643,
"dtype": "bfloat16",
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2560,
"initializer_range": 0.02,
"intermediate_size": 9728,
"layer_types": [
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"full_attention",
"full_attention",
"full_attention",
"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 40960,
"max_window_layers": 36,
"model_type": "qwen3",
"num_attention_heads": 32,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000,
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "4.57.1",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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{
"__version__": {
"sentence_transformers": "5.4.0"
},
"activation_fn": "torch.nn.modules.linear.Identity",
"default_prompt_name": null,
"model_type": "CrossEncoder",
"prompts": {}
}

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"temperature": 0.6,
"top_k": 20,
"top_p": 0.95,
"transformers_version": "4.57.1"
}

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1
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