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---
dataset_info:
features:
- name: query
dtype: string
- name: pos
dtype: string
- name: neg
dtype: string
- name: task_name
dtype: string
- name: query_instruct
dtype: string
- name: pos_instruct
dtype: string
- name: neg_instruct
dtype: string
splits:
- name: train
num_bytes: 2555303114
num_examples: 1435000
download_size: 1231001259
dataset_size: 2555303114
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# MEDI dataset
This dataset was used in the paper GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning. Refer to https://arxiv.org/abs/2402.16829 for details.
The original dataset comes from the paper "One Embedder, Any Task: Instruction-Finetuned Text Embeddings" (https://arxiv.org/abs/2212.09741), which was used to train the INSTRUCTOR family of models (GitHub: https://github.com/xlang-ai/instructor-embedding).
The code for processing and publishing the raw data to HuggingFace Hub is available at https://github.com/avsolatorio/GISTEmbed.
## Citation
**GISTEmbed**
```
@article{solatorio2024gistembed,
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
author={Aivin V. Solatorio},
journal={arXiv preprint arXiv:2402.16829},
year={2024},
URL={https://arxiv.org/abs/2402.16829}
eprint={2402.16829},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
**INSTRUCTOR**
```
@inproceedings{INSTRUCTOR,
title={One Embedder, Any Task: Instruction-Finetuned Text Embeddings},
author={Su, Hongjin and Shi, Weijia and Kasai, Jungo and Wang, Yizhong and Hu, Yushi and Ostendorf, Mari and Yih, Wen-tau and Smith, Noah A. and Zettlemoyer, Luke and Yu, Tao},
url={https://arxiv.org/abs/2212.09741},
year={2022},
}
``` |