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2f86d9b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | # PubMedQA Onboarding and Evaluation on HD DiffusionGemma
This folder contains the dataset ingestion and preprocessing for onboarding the
**PubMedQA** dataset.
PubMedQA is a medical question-answering dataset where the task is to read a
medical research abstract context and a question, and then:
1. Output a short categorical answer: **"yes"**, **"no"**, or **"maybe"**.
2. Generate a detailed paragraph-length explanation (**LONG_ANSWER**).
Our dataset tasks the model with generating both the categorical answer and the
detailed explanation.
## Directory Structure
* **`prepare_pubmedqa_dataset.sh`**: Clones the dataset from the GitHub repo
and calls the preprocessing script.
* **`convert_pubmedqa.py`**: Script that formats the raw datasets with
instruction-tuning turn tokens, and outputs them as line-delimited JSONL
files.
* **`pubmedqa_data.py`**: Implements `JsonlDataSource` (an
in-memory JSONL loader compatible with the Grain `RandomAccessDataSource`
Protocol) with slicing support to split training data.
## Expected Output
* **Train split**: `ori_pqal.json` (1,000 expert-labeled examples) **minus**
`test_set.json` (500 test IDs) → `pubmedqa_train.jsonl` (500 clean training
examples).
* **Test split**: `test_set.json` (500 official test examples) →
`pubmedqa_test.jsonl`.
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