| --- |
| license: apache-2.0 |
| tags: |
| - medical |
| - neurology |
| - neurosurgery |
| - search |
| - rag |
| - query-rewriting |
| datasets: |
| - miriad/miriad-4.4M |
| base_model: |
| - google/flan-t5-small |
| language: en |
| pipeline_tag: summarization |
| --- |
| |
| # NeuroRewriter: Neurology & Neurosurgery Query Optimizer |
|
|
| ## 🩺 Model Description |
| **NeuroRewriter** is a fine-tuned version of `google/flan-t5-small` specialized for the medical domains of **Neurology** and **Neurosurgery**. |
|
|
| Its primary function is to act as a **Query Rewriter** in RAG (Retrieval-Augmented Generation) pipelines. It transforms verbose, natural language user questions into concise, keyword-rich search strings. This "denoising" process strips away conversational fluff to focus on high-value medical entities (symptoms, anatomy, drug names, procedures). |
|
|
| ## 🚀 Intended Use & Best Practices |
|
|
| ### 1. RAG Pipeline Integration |
| This model is designed to sit between the User and your Vector Database/Search Engine. |
| * **Input:** "What are the common complications after a craniotomy?" |
| * **Output:** "craniotomy complications post-op" |
|
|
| ### 2. Retrieval Strategy (Important) |
| This model is optimized for **Keyword-Based Retrieval (Sparse Retrieval)** methods such as: |
| * **BM25** |
| * **TF-IDF** |
| * **Splade** |
| * **Elasticsearch / OpenSearch** |
|
|
| > **Note:** Because this model removes grammatical connectors ("stop words") to boost keyword density, it is **less effective** for pure dense vector retrieval (like OpenAI embeddings) which often relies on full sentence context. For best results, use a hybrid approach or pure BM25. |
|
|
| ## ⚠️ Limitations & Medical Disclaimer |
| **NOT FOR CLINICAL DIAGNOSIS.** |
| This model is intended for **informational retrieval purposes only**. |
| * It is not a doctor and should not be used to make medical decisions. |
| * While it improves search relevance, it may occasionally generate keywords that slightly alter the medical intent (e.g., confusing "acute" vs. "chronic" contexts). |
| * Always verify results against trusted medical sources. |
|
|
| ## 📚 Training Data |
| This model was fine-tuned on a curated subset of the **MIRIAD dataset** (MIRIAD: A Large-Scale Dataset for Medical Information Retrieval and Answer Discovery). |
| * **License:** ODC-By 1.0 |
| * **Attribution:** Zheng et al. (2025) |
|
|
| ## 💻 How to Use |
| ```python |
| # pip install transformers torch |
| |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
| |
| # 1. Load the model |
| model_name = "HugSena13/neroRewriter" |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_name) |
| |
| # 2. Prepare the input (Include the prefix used in training!) |
| input_text = "extract search keywords: What are the treatment options for glioblastoma multiforme?" |
| inputs = tokenizer(input_text, return_tensors="pt") |
| |
| # 3. Generate (Adjust max_new_tokens if output is cut off) |
| outputs = model.generate( |
| **inputs, |
| max_new_tokens=50, |
| num_beams=5, |
| early_stopping=True |
| ) |
| |
| # 4. Decode |
| result = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| print(result) |
