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--- |
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license: mit |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-1.5B-Instruct |
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library_name: transformers |
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tags: |
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- academic-knowledge-extraction |
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- concept-path-mining |
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- innovation-detection |
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- nsu-research |
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datasets: |
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- Hengzongshu/ArticleAgent |
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--- |
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# ArticleAgent: Constraint-Driven Qwen2.5-1.5B for Academic Concept Path Extraction |
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This repository hosts **ArticleAgent**, a fine-tuned **Qwen2.5-1.5B-Instruct** model designed to extract structured **concept paths** from academic paper abstracts. The model is part of the research presented in: |
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> **Constraint-Driven Small Language Models Based on Agent and OpenAlex Knowledge Graph: Mining Conceptual Pathways and Discovering Innovation Points in Academic Papers** |
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> Ziye Xia, Sergei S. Ospichev (2025) |
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The system leverages a **four-stage agent framework** grounded in the **OpenAlex knowledge graph**, combining prompt engineering, knowledge constraints, and human-in-the-loop validation to achieve high-precision concept extraction and novelty detection. |
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## 🔍 Key Features |
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- Extracts **structured concept paths** (e.g., `Physics → Condensed Matter → Superconductivity`) |
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- Identifies **innovation points** based on rare structural combinations of mainstream concepts |
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- Integrates **OpenAlex concept taxonomy** as external knowledge constraint |
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- Trained on **7,960 papers** from Novosibirsk State University (NSU) |
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- Achieves **97.24% precision** and **91.46% F1-score** in end-to-end concept path extraction |
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## 🚀 Usage |
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You can load the model directly using Hugging Face Transformers: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_name = "Hengzongshu/ArticleAgent" |
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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device_map="auto", |
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torch_dtype="bfloat16", |
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trust_remote_code=True |
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) |
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# Example input (Stage 2: Concept Pair Extraction) |
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input_text = """<research_methods>... your abstract segment ...</research_methods>""" |
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inputs = tokenizer(input_text, return_tensors="pt").to(model.device) |
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outputs = model.generate(**inputs, max_new_tokens=256) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
