metadata

language:
  - en
license: apache-2.0
tags:
  - text
pretty_name: MSMARCO
size_categories:
  - 100K<n<1M
source_datasets:
  - MSMARCO
task_categories:
  - sentence-similarity
dataset_info:
  config_name: default
  features:
    - name: query
      dtype: string
    - name: pos
      sequence:
        - name: doc
          dtype: string
        - name: score
          dtype: float
    - name: neg
      sequence:
        - name: doc
          dtype: string
        - name: score
          dtype: float
  splits:
    - name: train
      num_bytes: 89609915
      num_examples: 502939
    - name: test
      num_bytes: 969945
      num_examples: 43
    - name: dev
      num_bytes: 1206403
      num_examples: 6980
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/*
      - split: test
        path: data/test/*
      - split: dev
        path: data/dev/*

MSMARCO 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
    }
  ]
}

This is the original converted dataset with the following splits:

train: 502939 queries, only positives.
test: 43 queries, positives and negatives.
dev: 6980 queries, only positives.

Usage

from datasets import load_dataset

data = load_dataset('nixiesearch/msmarco', split="train")

License

Apache 2.0