初始化项目，由ModelHub XC社区提供模型
Model: InternScience/Agents-K1 Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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base_model: Qwen/Qwen3-4B-Instruct-2507
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tags:
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- information-extraction
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- named-entity-recognition
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- relation-extraction
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- grpo
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- reinforcement-learning
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- qwen3
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- scientific-text
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- biomedical
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---
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# Agents-K1
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**Knowledge extraction model in Agents-K1** is a 4B-parameter language model fine-tuned from
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[`Qwen/Qwen3-4B-Instruct-2507`](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
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with **GRPO** (Group Relative Policy Optimization) on the information-extraction
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corpus, targeting **Named Entity Recognition (NER)** and **Relation Extraction (RE)**
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in English scientific and general-domain text.
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The model produces structured JSON extractions with explicit step-by-step
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reasoning, enabling its use as a building block in downstream knowledge-graph
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construction, citation linking, and multi-hop QA pipelines.
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## Highlights
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- **+3.3 absolute F1** averaged over 10 NER/RE benchmarks vs. the
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Qwen3-4B-Instruct base model, with **gains on every dataset evaluated**
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(including held-out CrossNER domains).
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- Trained with rule-based rewards (format + JSON validity + entity/relation F1),
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no human preference data required.
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- Outputs follow a strict `<think>…</think><answer>…</answer>` schema, making
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reasoning auditable and JSON parsing reliable.
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## Intended use
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Designed as an extraction backbone for:
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- Scientific-literature mining (entities/relations in biomedicine, chemistry,
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CS, etc.)
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- Knowledge-graph construction
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- Pre-processing for retrieval / multi-hop QA systems
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Not intended for general-purpose chat — it has been specialized for structured
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extraction.
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## Usage
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The model uses the same chat template as Qwen3-4B-Instruct and expects a
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schema-driven user prompt. The reply will contain a `<think>` block followed by
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an `<answer>` block with a JSON object.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "InternScience/Agents-K1"
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tok = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="bfloat16", device_map="auto")
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system = (
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"You are an expert in information extraction. Given a task instruction "
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"with schema definitions and input text, extract the required information.\n\n"
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"You should think step by step about the extraction task, then provide "
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"your answer in JSON format.\n\n"
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"Format your response as:\n"
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"<think>\nYour step-by-step reasoning...\n</think>\n"
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"<answer>\nYour JSON extraction result here\n</answer>"
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)
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user = (
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"You are an expert in named entity recognition. Please extract entities "
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"that match the schema definition from the input. Return an empty list if "
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"the entity type does not exist. Please respond in the format of a JSON "
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"dictionary.\n\n"
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'Entity types to extract: ["person", "organization", "location"]\n\n'
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"Input text: Marie Curie worked at the University of Paris.\n\n"
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"Please think step by step and respond in the following format:\n"
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"<think>\nYour reasoning process...\n</think>\n"
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"<answer>\nYour JSON extraction result\n</answer>"
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)
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messages = [{"role": "system", "content": system},
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{"role": "user", "content": user}]
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inputs = tok.apply_chat_template(messages, add_generation_prompt=True,
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return_tensors="pt").to(model.device)
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out = model.generate(inputs, max_new_tokens=512, do_sample=False)
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print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
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```
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For RE, replace the user template with `Relation types to extract: [...]`
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and a relation-extraction instruction; the output schema is a JSON dict mapping
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relation types to lists of `{head, tail}` pairs.
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## Training data
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Training data comes from **IEPile**, restricted to:
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- English NER and RE tasks
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- 22 source datasets, mixing scientific (SciERC, GENIA_NER, BC5CDR, BC2GM,
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BC4CHEMD, AnatEM, NCBI) and general-domain (CoNLL2003, conll04, FabNER,
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MultiNERD, NYT11, kbp37, …) corpora
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| Split | Size | Notes |
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|-----------:|-------:|-------|
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| Train | 14,400 | 90/10 split, seed=42; each source capped to balance the mix |
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| Validation | 1,600 | |
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70% of samples have non-empty gold labels; 30% are empty-label cases (to prevent
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the model from defaulting to non-empty outputs).
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## Training procedure
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- **Algorithm:** GRPO (PPO without a critic), implemented in
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[veRL](https://github.com/volcengine/verl).
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- **Reward** ∈ \[0, 1\]:
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- format reward: `0.1 · 𝟙[has <think>] + 0.1 · 𝟙[has <answer>]`
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- JSON validity: `0.1 · 𝟙[valid JSON dict]` (or `0.05` for non-dict valid JSON)
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- task F1: `0.7 · F1(pred, gold)` — entity-set F1 for NER, triple-set F1 for RE
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## Evaluation
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Reported numbers are micro-F1 on each benchmark's official test split, using
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the same prompt template as training. Gains are **base → Agents-K1 (GRPO)**.
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| Dataset | Task | n | Base F1 | Agent-K1 F1 | Δ |
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|---------------------------------|:----:|------:|--------:|--------------:|------:|
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| CoNLL2003 | NER | 3,184 | 0.6547 | **0.7007** | +0.046 |
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| NCBI-Disease | NER | 937 | 0.6737 | **0.7340** | +0.060 |
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| BC5CDR | NER | 4,788 | 0.7126 | **0.7494** | +0.037 |
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| CrossNER — AI *(held-out)* | NER | 430 | 0.4862 | **0.5400** | +0.054 |
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| CrossNER — Literature *(held)* | NER | 416 | 0.5462 | **0.5736** | +0.027 |
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| CrossNER — Music *(held)* | NER | 457 | 0.5791 | **0.6050** | +0.026 |
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| CrossNER — Politics *(held)* | NER | 650 | 0.6611 | **0.6855** | +0.024 |
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| CrossNER — Science *(held)* | NER | 532 | 0.5928 | **0.6132** | +0.020 |
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| SciERC | NER | 397 | 0.1166 | **0.1270** | +0.010 |
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| conll04 | RE | 287 | 0.2933 | **0.3181** | +0.025 |
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| **Average** | | | 0.5317 | **0.5647** | **+0.033** |
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All 10/10 benchmarks improve, including the 5 CrossNER domains that are
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**not** in the training mix — evidence of generalization rather than mere
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fitting to in-distribution sources.
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## Limitations
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- **Schema-driven prompting required.** Free-form questions will likely
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return malformed JSON; always supply explicit entity / relation type lists.
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## License
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Released under the **Apache-2.0** license, following the upstream
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[Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
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license. Users must also comply with the licenses of the IEPile component
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datasets when using this model in derivative works.
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