| --- |
| pretty_name: LabOPBench |
| language: |
| - en |
| task_categories: |
| - visual-question-answering |
| configs: |
| - config_name: all |
| data_files: |
| - split: test |
| path: data/*.jsonl |
| - config_name: Experiment_Character |
| data_files: |
| - split: test |
| path: data/Experiment_Character.jsonl |
| - config_name: Experiment_Monitor |
| data_files: |
| - split: test |
| path: data/Experiment_Monitor.jsonl |
| - config_name: Experiment_Postprocess |
| data_files: |
| - split: test |
| path: data/Experiment_Postprocess.jsonl |
| - config_name: Experiment_Preparation |
| data_files: |
| - split: test |
| path: data/Experiment_Preparation.jsonl |
| - config_name: material_science |
| data_files: |
| - split: test |
| path: data/material_science.jsonl |
| --- |
| |
| # LabOPBench |
|
|
| LabOPBench is a benchmark for evaluating multimodal large language models in realistic laboratory scenarios. It is designed to assess their capabilities in experimental workflow reasoning, safety and anomaly assessment, operational decision-making, and result analysis. |
|
|
| GitHub: [johnnylee00/LabOPBench](https://github.com/johnnylee00/LabOPBench) |
|
|
| This private partial release contains 100 questions: 20 examples from each major category. Each record includes the question, image path(s), and A-D scoring answers. The full benchmark will be expanded after the paper is released. |
|
|
| ## Files |
|
|
| - `data/Experiment_Character.jsonl` |
| - `data/Experiment_Monitor.jsonl` |
| - `data/Experiment_Postprocess.jsonl` including TLC examples |
| - `data/Experiment_Preparation.jsonl` |
| - `data/material_science.jsonl` |
| - `images/` contains all referenced PNG files |
| - `examples/evaporation_example.json` is a qualitative model-response example and is not part of the formal HF dataset configs |
|
|
| ## Data Format |
|
|
| Each JSONL row uses this structure: |
|
|
| ```json |
| { |
| "id": "...", |
| "major_category": "Experiment_Postprocess", |
| "category": "TLC", |
| "item_no": "TLC_001", |
| "question": "...", |
| "image": ["TLC/TLC_001_01.png"], |
| "answers": ["A: ... (3 points)", "B: ... (2 points)", "C: ... (1 point)", "D: ... (0 points)"] |
| } |
| ``` |
|
|
| The `image` entries are relative paths under the repository's `images/` directory. |
|
|
| ## Quick Start |
|
|
| For private access, log in first: |
|
|
| ```bash |
| hf auth login |
| ``` |
|
|
| Download the dataset repository and run an evaluation: |
|
|
| ```bash |
| git clone https://github.com/johnnylee00/LabOPBench.git |
| cd LabOPBench |
| python -m pip install -e ".[openai]" |
| |
| export OPENAI_API_KEY="<your-api-key>" |
| export OPENAI_BASE_URL="<your-base-url>" |
| MODEL="<model-name>" |
| |
| hf download JOHNNYlee1/LabOPBench --repo-type dataset --local-dir data/LabOPBench |
| |
| labopbench run --data data/LabOPBench/data/Experiment_Postprocess.jsonl --image-base-dir data/LabOPBench/images --model-name "$MODEL" --out outputs/experiment_postprocess_answers.json |
| |
| labopbench score --pred outputs/experiment_postprocess_answers.json --data data/LabOPBench/data/Experiment_Postprocess.jsonl --out outputs/experiment_postprocess_scores.json |
| ``` |
|
|
| ## Release Note |
|
|
| This is an initial private release for repository setup and workflow validation. It should not be treated as the complete benchmark or as a hidden-test leaderboard set. |
|
|
