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---
language:
- en
license: apache-2.0
tags:
- text
pretty_name: MS MARCO
size_categories:
- "100K<n<1M"
source_datasets:
- MSMARCO
task_categories:
- sentence-similarity
dataset_info:
config_name: default
features:
- name: query
dtype: string
- name: positive
sequence:
- dtype: string
- name: negative
sequence:
- dtype: string
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/*"
---
# MS MARCO dataset
A dataset in a [nixietune](https://github.com/nixiesearch/nixietune) compatible format:
```json
{
"query": ")what was the immediate impact of the success of the manhattan project?",
"positive": [
"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."
],
"negative": []
}
```
This is the original [BeIR/msmarco](https://huggingface.co/datasets/BeIR/msmarco) converted dataset with the following splits:
* train: 502939 queries, only positives.
* test: 43 queries, positives and negatives.
* dev: 6980 queries, only positives.
## Usage
```python
from datasets import load_dataset
data = load_dataset('nixiesearch/ms_marco')
print(data["train"].features)
```
## License
Apache 2.0 |