---
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}
}
```