Update README.md

README.md

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 ---
+license: apache-2.0
 base_model: Qwen/Qwen3-4B
-library_name: peft
+tags:
+- boolean-queries
+- systematic-review
+- information-retrieval
+- pubmed
+- reinforcement-learning
+- grpo
+- chain-of-thought
+library_name: transformers
 ---
 
-# Model Card for Model ID
+# AutoBool-Qwen4b-Reasoning
 
-<!-- Provide a quick summary of what the model is/does. -->
+This model is part of the **AutoBool** framework, a reinforcement learning approach for training large language models to generate high-quality Boolean queries for systematic literature reviews.
 
+## Model Description
 
+This variant uses **explicit chain-of-thought reasoning**. The model is instructed to provide detailed reasoning about the query construction process inside `<think></think>` tags before generating the final Boolean query.
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
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-### Model Sources [optional]
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-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
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-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+- **Base Model:** Qwen/Qwen2.5-4B
+- **Training Method:** GRPO (Group Relative Policy Optimization) with LoRA fine-tuning
+- **Prompt Strategy:** Chain-of-thought reasoning
+  - System instruction: "Your reasoning process should be enclosed within `<think></think>`, and the final Boolean query must be enclosed within `<answer></answer>` tags"
+  - Output format: `<think>[Detailed step-by-step reasoning explaining the query construction process]</think><answer>[Boolean query]</answer>`
+  - Provides full explanation of term selection, MeSH terms, field tags, wildcards, and Boolean logic
+- **Domain:** Biomedical literature search (PubMed)
+- **Task:** Boolean query generation for high-recall retrieval
 
 ## Training Details
 
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
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-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
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-#### Factors
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-#### Metrics
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-### Results
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-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
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-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
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-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
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-### Compute Infrastructure
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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-### Framework versions
-
-- PEFT 0.15.2
+The model was trained using:
+- **Optimization:** GRPO (Group Relative Policy Optimization)
+- **Fine-tuning:** LoRA (Low-Rank Adaptation)
+- **Dataset:** wshuai190/pubmed-pmc-sr-filtered
+- **Reward Function:** Combines syntactic validity, format correctness, and retrieval effectiveness
+- **Reasoning Approach:** Explicit thinking process with structured tags
+
+## Intended Use
+
+This model is designed for:
+- Generating Boolean queries for systematic literature reviews
+- High-recall biomedical information retrieval
+- Supporting evidence synthesis in healthcare and biomedical research
+- Applications where reasoning transparency is valuable
+
+## How to Use
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import re
+
+model_name = "ielabgroup/Autobool-Qwen4b-Reasoning"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Define your systematic review topic
+topic = "Diagnostic accuracy of endoscopic ultrasonography (EUS) for the preoperative locoregional staging of primary gastric cancer"
+
+# Construct the prompt with system and user messages
+messages = [
+    {"role": "system", "content": "You are an expert systematic review information specialist.\nYou are tasked to formulate a systematic review Boolean query in response to a research topic.\nYour reasoning process should be enclosed within <think></think>, and the final Boolean query must be enclosed within <answer></answer> tags. Do not include anything outside of these tags."},
+    {"role": "user", "content": f'You are given a systematic review research topic, with the topic title "{topic}".\nYour task is to generate a highly effective Boolean query in MEDLINE format for PubMed.\nThe query should balance **high recall** (capturing all relevant studies) with **reasonable precision** (avoiding irrelevant results):\n- Use both free-text terms and MeSH terms (e.g., chronic pain[tiab], Pain[mh]).\n- **Do not wrap terms or phrases in double quotes**, as this disables automatic term mapping (ATM).\n- Combine synonyms or related terms within a concept using OR.\n- Combine different concepts using AND.\n- Use wildcards (*) to capture word variants (e.g., vaccin* → vaccine, vaccination):\n  - Terms must have ≥4 characters before the * (e.g., colo*)\n  - Wildcards work with field tags (e.g., breastfeed*[tiab]).\n- Field tags limit the search to specific fields and disable ATM.\n- Do not include date limits.\n- Tag terms using appropriate fields (e.g., covid-19[ti] vaccine[ti] children[ti]) when needed.\n**Only use the following allowed field tags:**\nTitle: [ti], Abstract: [ab], Title/Abstract: [tiab]\nMeSH: [mh], Major MeSH: [majr], Supplementary Concept: [nm]\nText Words: [tw], All Fields: [all]\nPublication Type: [pt], Language: [la]\n\nOutput your full reasoning inside <think></think>.\nOutput the final Boolean query inside <answer></answer>.\nDo not include any content outside these tags.'}
+]
+
+# Generate the query
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=4096)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Extract reasoning and query
+reasoning_match = re.search(r'<think>(.*?)</think>', response, re.DOTALL)
+query_match = re.search(r'<answer>(.*?)</answer>', response, re.DOTALL)
+
+if reasoning_match and query_match:
+    reasoning = reasoning_match.group(1).strip()
+    query = query_match.group(1).strip()
+    print("Reasoning:", reasoning)
+    print("\nQuery:", query)
+```
+
+The model will generate output with reasoning:
+```
+<think>
+[Detailed step-by-step reasoning explaining the query construction process,
+including term selection, MeSH terms, field tags, wildcards, and Boolean logic]
+</think>
+<answer>
+[Final Boolean query]
+</answer>
+```
+
+## Advantages
+
+- Provides interpretable reasoning process
+- Can help understand query construction decisions
+- May improve query quality through structured thinking
+
+## Limitations
+
+- Optimized specifically for PubMed Boolean query syntax
+- Performance may vary on non-biomedical domains
+- Requires domain knowledge for effective prompt engineering
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@inproceedings{autobool2026,
+  title={AutoBool: Reinforcement Learning for Boolean Query Generation in Systematic Reviews},
+  author={Shuai Wang, Harrisen Scells, Bevan Koopman, Guido Zuccon},
+  booktitle={Proceedings of the 2026 Conference of the European Chapter of the Association for Computational Linguistics (EACL)},
+  year={2026}
+}
+```
+
+## More Information
+
+- **GitHub Repository:** [https://github.com/ielab/AutoBool](https://github.com/ielab/AutoBool)
+- **Paper:** Accepted at EACL 2026
+
+## License
+
+Apache 2.0