Datasets:
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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.
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