Update README.md

README.md

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 - **Name**: SIMORD  
 - **Full name / acronym**: SIMulated ORDer Extraction  
 - **Purpose / use case**:  
-  SIMORD is intended to support research in extracting structured medical orders (e.g. medication orders, lab orders) from doctor-patient consultation transcripts. It complements the SYNUR dataset by focusing on the downstream task of converting spoken clinical dialogue into structured orders. :contentReference[oaicite:0]{index=0}  
-- **Version**: As released with the paper (2025)  
+  SIMORD is intended to support research in extracting structured medical orders (e.g. medication orders, lab orders) from doctor-patient consultation transcripts.
+- **Version**: As released with the EMNLP industry track paper (2025)  
 - **License / usage terms**: CDLA-2.0-permissive  
 - **Contact / Maintainer**: jcorbeil@microsoft.com  
 
+## Building the dataset
+
+### Method 1: HF datasets
+
+1. Make sure you have `datasets==3.6.0` or less, otherwise builder is not supported in recent versions.
+2. Git clone and install requirements from `https://github.com/jpcorb20/mediqa-oe`
+3. Add `mediqa-oe` to python path `PYTHONPATH=$PYTHONPATH:/mypath/to/mediqa_oe` (UNIX).
+4. Run `load_dataset("microsoft/SIMORD", trust_remote_code=True)`, which will merge transcripts from ACI-Bench and Primock57 repos into the annotation files.
+
+### Method 2: GitHub script
+
+Follow the steps in `https://github.com/jpcorb20/mediqa-oe` to merge transcripts from ACI-Bench and Primock57 into the annotation files provided in the repo.
+
 ## 4. Data Fields / Format
 
 - **Input fields**:  
-  - `transcript`: string, the doctor-patient consultation transcript (with disfluencies, interruptions, etc.)  
-  - `schema`: metadata of the target order schema (possible order types, attributes)  
+  - `transcript` (dict of list): the doctor-patient consultation transcript as dict of three lists using those keys:
+    - `turn_id` (int): index of that turn.
+    - `speaker` (str): speaker of that turn *DOCTOR* or *PATIENT*.
+    - `transcript` (str): line of that turn.
 
-- **Output / label fields**:  
-  - A JSON (or list) of **order objects**  
+- **Output fields**:  
+  - A JSON (or list) of **expected orders**  
   - Each order object includes at least:  
     * `order_type` (e.g. “medication”, “lab”)  
     * `description` (string) — the order text (e.g. “lasix 40 milligrams a day”)  
     * `reason` (string) — the clinical reason or indication for the order  
     * `provenance` (e.g. list of token indices or spans) — mapping back to parts of the transcript  
 
-- **Annotation format constraints**: Outputs must conform to a parsable JSON format consistent with the schema defined in each example.  
+## Splits
+
+- `train`: examples for in-context learning or fine-tuning.
+- `test1`: test set used for the EMNLP 2025 industry track paper. Also, previously named `dev` set for MEDIQA-OE shared task of ClinicalNLP 2025.
+- `test2`: test set for MEDIQA-OE shared task of ClinicalNLP 2025.
 
 ## Citation
 
+If you use this dataset, please cite:
+
     @article{corbeil2025empowering,
       title={Empowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applications},
       author={Corbeil, Jean-Philippe and Abacha, Asma Ben and Michalopoulos, George and Swazinna, Phillip and Del-Agua, Miguel and Tremblay, Jerome and Daniel, Akila Jeeson and Bader, Cari and Cho, Yu-Cheng and Krishnan, Pooja and others},