
<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}
}

multilinguality:monolingual size_categories:1K<n<10K language:en license:apache-2.0 finetuner arxiv:2307.11224 region:us
{"language": "en", "license": "apache-2.0", "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "tags": ["finetuner"], "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}}
TAGS
#multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-apache-2.0 #finetuner #arxiv-2307.11224 #region-us 

  
  




![](URL alt=)




**The data offered by Jina AI, Finetuner team.**



Summary
-------


This dataset is an English-language dataset based on the 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: # of items, test: 10000


Source
------


Positive pairs were sampled from the SNLI dataset and negative samples were created using GPT-3.5
and GPT-4.


Example Usage
-------------


Output:


Models
------


Jina AI's open source embedding models (small,
base and
large) 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 for adding this dataset.


Contact
-------


Join our Discord community and chat with other community members about ideas.


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}
}



# of items, test: 10000


Source
------


Positive pairs were sampled from the SNLI dataset and negative samples were created using GPT-3.5
and GPT-4.


Example Usage
-------------


Output:


Models
------


Jina AI's open source embedding models (small,
base and
large) 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 for adding this dataset.


Contact
-------


Join our Discord community and chat with other community members about ideas.


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}
}
TAGS
#multilinguality-monolingual #size_categories-1K<n<10K #language-English #license-apache-2.0 #finetuner #arxiv-2307.11224 #region-us 
 # of items, test: 10000


Source
------


Positive pairs were sampled from the SNLI dataset and negative samples were created using GPT-3.5
and GPT-4.


Example Usage
-------------


Output:


Models
------


Jina AI's open source embedding models (small,
base and
large) 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 for adding this dataset.


Contact
-------


Join our Discord community and chat with other community members about ideas.


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}
}