metadata

language:
  - en
license: apache-2.0
tags:
  - text
pretty_name: MS MARCO hard negatives
size_categories:
  - 100K<n<1M
source_datasets:
  - BeIR/msmarco
  - sentence-transformers/msmarco-hard-negatives
task_categories:
  - sentence-similarity
dataset_info:
  config_name: default
  features:
    - name: query
      dtype: string
    - name: pos
      list:
        - name: doc
          dtype: string
        - name: score
          dtype: float
    - name: neg
      list:
        - name: doc
          dtype: string
        - name: score
          dtype: float
  splits:
    - name: train
      num_bytes: 89609915
      num_examples: 502939
train-eval-index:
  - config: default
    task: sentence-similarity
    splits:
      train_split: train
      eval_split: test
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train/*

MS MARCO hard negatives dataset

A dataset in a nixietune compatible format:

{
  "query": ")what was the immediate impact of the success of the manhattan project?",
  "pos": [
    {
      "doc": "The presence of communication amid scientific minds was equally important to the success of the Manhattan Project as scientific intellect was. The only cloud hanging over the impressive achievement of the atomic researchers and engineers is what their success truly meant; hundreds of thousands of innocent lives obliterated.",
      "score": 1
    }
  ],
  "neg": [
    {
      "doc": "Abstract. The pivotal engineering and scientific success of the Twentieth century was the Manhattan Project. The Manhattan Project assimilated concepts and leaders from all scientific fields and engineering disciplines to construct the first two atomic bombs.",
      "score": 0.0
    },
    {
      "doc": "The pivotal engineering and scientific success of the Twentieth century was the Manhattan Project. The Manhattan Project assimilated concepts and leaders from all scientific fields and engineering disciplines to construct the first two atomic bombs.",
      "score": 0.0
    }
  ]
}

This is the original BeIR-msmarco joined with the msmarco-hard-negatives dataset with the following splits:

train: 502939 queries, only positives.

Usage

from datasets import load_dataset

data = load_dataset('nixiesearch/ms-marco-hard-negatives')
print(data["train"].features)

License

Apache 2.0