cmudrc/Material_Selection_Eval
A benchmark designed to facilitate evaluation and modify the behavior of a foundation model through different existing techniques in the context of material selection for conceptual design. The data is collected by conducting a survey of experts in the field of material selection. The same questions mentioned in keyquestions.csv are asked to experts. This can be used to evaluate a Language model performance and its spread compared to a human evaluation. To get into a more detailed explanation… See the full description on the dataset page: https://huggingface.co/datasets/cmudrc/Material_Selection_Eval.
MSEval Dataset: ---
A benchmark designed to facilitate evaluation and modify the behavior of a foundation model through different existing techniques in the context of material selection for conceptual design.
The data is collected by conducting a survey of experts in the field of material selection. The same questions mentioned in keyquestions.csv are asked to experts.
This can be used to evaluate a Language model performance and its spread compared to a human evaluation.
To get into a more detailed explanation - use this link [https://arxiv.org/abs/2407.09719v1]
Overview
We introduce MSEval, a benchmark derived from survey results of experts in the field of material selection.
The MixEval consists of two files: CleanResponses and AllResponses. Below presents the dataset file tree:
MSEval
│
├── AllResponses.csv
└── CleanResponses.csv
└── KeyQuestions.csvDataset Usage
An example use of the dataset using the datasets library is shown in https://github.com/cmudrc/MSEval
To use this dataset using pandas:
import pandas as pd
df = pd.read_csv("hf://datasets/cmudrc/Material_Selection_Eval/AllResponses.csv")Replace AllResponses with CleanResponses and KeyQuestions in the pathname if required.
Citation
If you found the dataset useful, please cite:
@misc{jain2024msevaldatasetmaterialselection,
title={MSEval: A Dataset for Material Selection in Conceptual Design to Evaluate Algorithmic Models},
author={Yash Patawari Jain and Daniele Grandi and Allin Groom and Brandon Cramer and Christopher McComb},
year={2024},
eprint={2407.09719},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2407.09719},
}license: mit ---
