multilinguality:monolingual size_categories:1K<n<10K language:en license:apache-2.0 finetuner arxiv:2307.11224 region:us

---
tags:
  - finetuner
language: en
license: apache-2.0
dataset_info:
  features:
  - name: anchor
    dtype: string
  - name: entailment
    dtype: string
  - name: negative
    dtype: string
  splits:
  - name: train
    num_examples: 10000
  - name: test
    num_examples: 500
  download_size: 1467517
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
---

<br><br>

<p align="center">
<img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
</p>


<p align="center">
<b>The data offered by Jina AI, Finetuner team.</b>
</p>

## Summary

This dataset is an English-language dataset based on the [SNLI](https://huggingface.co/datasets/snli) dataset.
It contains negations of samples from SNLI.

## Instances

Each data point consists of a triplet ('anchor', 'entailment', 'negative') of strings, where ('anchor', 'entailment') are positive pairs
taken from SNLI, and 'negative' contradicts  both 'anchor' and 'entailment'.

## Fields

- 'anchor': string, some statement
- 'entailment': string, a statement which follows from 'anchor', but is usually syntactically dissimilar
- 'negative': string, a statement contradicting 'anchor' and 'entailment'. Syntactically very similar to 'entailment'

## Splits

|            | train | test |
|------------|-------|------|
| # of items | 10000 | 500  |

## Source

Positive pairs were sampled from the [SNLI](https://huggingface.co/datasets/snli) dataset and negative samples were created using GPT-3.5
and GPT-4.

## Example Usage

```python
from datasets import load_dataset
from pprint import pprint

dataset = load_dataset('jinaai/negation-dataset')
pprint(dataset['train'][:5])
```

Output:

```python
{'anchor': ['Two young girls are playing outside in a non-urban environment.',
            'A man with a red shirt is watching another man who is standing on '
            'top of a attached cart filled to the top.',
            'A man in a blue shirt driving a Segway type vehicle.',
            'A woman holds her mouth wide open as she is placing a stack of '
            'crackers in.',
            'A group of people standing on a rock path.'],
 'entailment': ['Two girls are playing outside.',
                'A man is standing on top of a cart.',
                'A person is riding a motorized vehicle.',
                'There is a woman eating crackers.',
                'A group of people are hiking.'],
 'negative': ['Two girls are not playing outside.',
              'A man is not standing on top of a cart.',
              'A person is not riding a motorized vehicle.',
              'There is no woman eating crackers.',
              'A group of people are not hiking.']}
```

## Models

[Jina AI's](https://jina.ai) open source embedding models ([small](https://huggingface.co/jinaai/jina-embedding-s-en-v1),
[base](https://huggingface.co/jinaai/jina-embedding-b-en-v1) and 
[large](https://huggingface.co/jinaai/jina-embedding-l-en-v1)) were all fine-tuned on the negation dataset.

## Licensing Information

This work is licensed under the Apache License, Version 2.0.

## Contributors

Thanks to contributors from [Jina AI](https://jina.ai) for adding this dataset.

## Contact

Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.

## Citation

If you find this dataset useful in your research, please cite the following paper:

``` latex
@misc{günther2023jina,
      title={Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models}, 
      author={Michael Günther and Louis Milliken and Jonathan Geuter and Georgios Mastrapas and Bo Wang and Han Xiao},
      year={2023},
      eprint={2307.11224},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
