Datasets:

Modalities:
Text
Formats:
json
Languages:
English
Tags:
text
Libraries:
Datasets
Dask
License:
bfhnd-small / README.md
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---
language:
- en
license: apache-2.0
tags:
- text
pretty_name: "BFHND: Big Hard Negatives Dataset (1M sample)"
size_categories:
- "100K<n<1M"
source_datasets:
- "BeIR"
task_categories:
- sentence-similarity
dataset_info:
config_name: default
features:
- name: query
dtype: string
- name: positive
sequence: string
- name: negative
sequence: string
splits:
- name: train
num_bytes: 226515502
num_examples: 1000000
train-eval-index:
- config: default
task: sentence-similarity
splits:
train_split: train
configs:
- config_name: default
data_files:
- split: train
path: "data/train/*"
---
# Big Hard Negatives Dataset
A dataset for training embedding models for semantic search.
TODO: add desc
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?",
"pos": [
"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."
],
"neg": [
"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.",
"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."
]
}
```
## Usage
To use with HF datasets:
```bash
pip install datasets zstandard
```
```python
from datasets import load_dataset
data = load_dataset('nixiesearch/bfhardneg-small')
print(data["train"].features)
```
## License
Apache 2.0