README.md

metadata

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}
}