---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': social
          '1': transport
          '2': calendar
          '3': play
          '4': news
          '5': datetime
          '6': recommendation
          '7': email
          '8': iot
          '9': general
          '10': audio
          '11': lists
          '12': qa
          '13': cooking
          '14': takeaway
          '15': music
          '16': alarm
          '17': weather
  - name: label_text
    dtype: string
  - name: text
    dtype: string
  - name: idx
    dtype: int64
  - name: query_idx
    dtype: int64
  - name: positive_idx
    dtype: int64
  - name: negative_idx
    dtype: int64
  splits:
  - name: train
    num_bytes: 1119338
    num_examples: 11514
  download_size: 644764
  dataset_size: 1119338
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---
# MTEB Amazon Massive Scenario Triplets 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 code for generating the data is available at https://github.com/avsolatorio/GISTEmbed/blob/main/scripts/create_classification_dataset.py.


## Citation
```
@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}
}
```