metadata

dataset_info:
  features:
    - name: id
      dtype: string
    - name: label
      dtype:
        class_label:
          names:
            '0': datetime_query
            '1': iot_hue_lightchange
            '2': transport_ticket
            '3': takeaway_query
            '4': qa_stock
            '5': general_greet
            '6': recommendation_events
            '7': music_dislikeness
            '8': iot_wemo_off
            '9': cooking_recipe
            '10': qa_currency
            '11': transport_traffic
            '12': general_quirky
            '13': weather_query
            '14': audio_volume_up
            '15': email_addcontact
            '16': takeaway_order
            '17': email_querycontact
            '18': iot_hue_lightup
            '19': recommendation_locations
            '20': play_audiobook
            '21': lists_createoradd
            '22': news_query
            '23': alarm_query
            '24': iot_wemo_on
            '25': general_joke
            '26': qa_definition
            '27': social_query
            '28': music_settings
            '29': audio_volume_other
            '30': calendar_remove
            '31': iot_hue_lightdim
            '32': calendar_query
            '33': email_sendemail
            '34': iot_cleaning
            '35': audio_volume_down
            '36': play_radio
            '37': cooking_query
            '38': datetime_convert
            '39': qa_maths
            '40': iot_hue_lightoff
            '41': iot_hue_lighton
            '42': transport_query
            '43': music_likeness
            '44': email_query
            '45': play_music
            '46': audio_volume_mute
            '47': social_post
            '48': alarm_set
            '49': qa_factoid
            '50': calendar_set
            '51': play_game
            '52': alarm_remove
            '53': lists_remove
            '54': transport_taxi
            '55': recommendation_movies
            '56': iot_coffee
            '57': music_query
            '58': play_podcasts
            '59': lists_query
    - 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: 1202479
      num_examples: 11514
  download_size: 658224
  dataset_size: 1202479
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

MTEB Amazon Massive Intent 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}
}