
## Dataset Description

- **Homepage:** https://github.com/gijswijnholds/sick_nl
- **Repository:** https://github.com/gijswijnholds/sick_nl
- **Paper:** https://aclanthology.org/2021.eacl-main.126/
- **Point of Contact:** [Gijs Wijnholds](mailto:gijswijnholds@gmail.com)

### Dataset Summary

An automatically translated, manually corrected translation of the SICK dataset of [Marelli et al. 2014](https://www.aclweb.org/anthology/L14-1314), intended to boost research in Dutch NLP.


### Languages

The dataset is in Dutch.

## Dataset Structure

### Data Fields

- pair_ID: sentence pair ID
- sentence_A: sentence A
- sentence_B: sentence B
- label: textual entailment gold label: entailment (0), neutral (1) or contradiction (2)
- relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale)
- entailment_AB: entailment for the A-B order (A_neutral_B, A_entails_B, or A_contradicts_B)
- entailment_BA: entailment for the B-A order (B_neutral_A, B_entails_A, or B_contradicts_A)
- sentence_A_original: original sentence from which sentence A is derived
- sentence_B_original: original sentence from which sentence B is derived
- sentence_A_dataset: dataset from which the original sentence A was extracted (FLICKR vs. SEMEVAL)
- sentence_B_dataset: dataset from which the original sentence B was extracted (FLICKR vs. SEMEVAL)

### Data Splits

Train Trial Test
4439 495 4906

## Dataset Creation

The dataset was created by first automatically translating all sentences, then by manually correcting any translation errors. This guarantees naturality of the examples while aligning the relatedness scores and entailment labels. Since the data IDs are preserved the dataset is fully aligned on the sentence level.

## Additional Information

### Licensing Information

This dataset falls under an MIT License.

### Citation Information


```
@inproceedings{wijnholds-etal-2021-sicknl,
    title = "SICK-NL: A Dataset for Dutch Natural Language Inference",
    author = "Wijnholds, Gijs and Moortgat, Michael",
    booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.eacl-main.126/",
}
```

### Contributions

Thanks to [@maximedb](https://huggingface.co/maximedb) for adding this dataset.
task_categories:text-classification size_categories:1K<n<10K language:nl license:mit region:us
{"language": ["nl"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "SICK-NL", "dataset_info": {"features": [{"name": "pair_ID", "dtype": "int64"}, {"name": "sentence_A", "dtype": "string"}, {"name": "sentence_B", "dtype": "string"}, {"name": "entailment_label", "dtype": "string"}, {"name": "relatedness_score", "dtype": "float64"}, {"name": "entailment_AB", "dtype": "string"}, {"name": "entailment_BA", "dtype": "string"}, {"name": "sentence_A_original", "dtype": "string"}, {"name": "sentence_B_original", "dtype": "string"}, {"name": "sentence_A_dataset", "dtype": "string"}, {"name": "sentence_B_dataset", "dtype": "string"}, {"name": "SemEval_set", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_seq2seq", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1359887, "num_examples": 4439}, {"name": "validation", "num_bytes": 153417, "num_examples": 495}, {"name": "test", "num_bytes": 1496660, "num_examples": 4906}], "download_size": 822658, "dataset_size": 3009964}}
TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-Dutch #license-mit #region-us 


## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL
- Point of Contact: Gijs Wijnholds

### Dataset Summary

An automatically translated, manually corrected translation of the SICK dataset of Marelli et al. 2014, intended to boost research in Dutch NLP.


### Languages

The dataset is in Dutch.

## Dataset Structure

### Data Fields

- pair_ID: sentence pair ID
- sentence_A: sentence A
- sentence_B: sentence B
- label: textual entailment gold label: entailment (0), neutral (1) or contradiction (2)
- relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale)
- entailment_AB: entailment for the A-B order (A_neutral_B, A_entails_B, or A_contradicts_B)
- entailment_BA: entailment for the B-A order (B_neutral_A, B_entails_A, or B_contradicts_A)
- sentence_A_original: original sentence from which sentence A is derived
- sentence_B_original: original sentence from which sentence B is derived
- sentence_A_dataset: dataset from which the original sentence A was extracted (FLICKR vs. SEMEVAL)
- sentence_B_dataset: dataset from which the original sentence B was extracted (FLICKR vs. SEMEVAL)

### Data Splits

Train Trial Test
4439 495 4906

## Dataset Creation

The dataset was created by first automatically translating all sentences, then by manually correcting any translation errors. This guarantees naturality of the examples while aligning the relatedness scores and entailment labels. Since the data IDs are preserved the dataset is fully aligned on the sentence level.

## Additional Information

### Licensing Information

This dataset falls under an MIT License.






### Contributions

Thanks to @maximedb for adding this dataset.
## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL
- Point of Contact: Gijs Wijnholds ### Dataset Summary

An automatically translated, manually corrected translation of the SICK dataset of Marelli et al. 2014, intended to boost research in Dutch NLP. ### Languages

The dataset is in Dutch. ## Dataset Structure ### Data Fields

- pair_ID: sentence pair ID
- sentence_A: sentence A
- sentence_B: sentence B
- label: textual entailment gold label: entailment (0), neutral (1) or contradiction (2)
- relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale)
- entailment_AB: entailment for the A-B order (A_neutral_B, A_entails_B, or A_contradicts_B)
- entailment_BA: entailment for the B-A order (B_neutral_A, B_entails_A, or B_contradicts_A)
- sentence_A_original: original sentence from which sentence A is derived
- sentence_B_original: original sentence from which sentence B is derived
- sentence_A_dataset: dataset from which the original sentence A was extracted (FLICKR vs. SEMEVAL)
- sentence_B_dataset: dataset from which the original sentence B was extracted (FLICKR vs. SEMEVAL) ### Data Splits

Train Trial Test
4439 495 4906 ## Dataset Creation

The dataset was created by first automatically translating all sentences, then by manually correcting any translation errors. This guarantees naturality of the examples while aligning the relatedness scores and entailment labels. Since the data IDs are preserved the dataset is fully aligned on the sentence level. ## Additional Information ### Licensing Information

This dataset falls under an MIT License. ### Contributions

Thanks to @maximedb for adding this dataset.
TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-Dutch #license-mit #region-us 
 ## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL
- Point of Contact: Gijs Wijnholds ### Dataset Summary

An automatically translated, manually corrected translation of the SICK dataset of Marelli et al. 2014, intended to boost research in Dutch NLP. ### Languages

The dataset is in Dutch. ## Dataset Structure ### Data Fields

- pair_ID: sentence pair ID
- sentence_A: sentence A
- sentence_B: sentence B
- label: textual entailment gold label: entailment (0), neutral (1) or contradiction (2)
- relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale)
- entailment_AB: entailment for the A-B order (A_neutral_B, A_entails_B, or A_contradicts_B)
- entailment_BA: entailment for the B-A order (B_neutral_A, B_entails_A, or B_contradicts_A)
- sentence_A_original: original sentence from which sentence A is derived
- sentence_B_original: original sentence from which sentence B is derived
- sentence_A_dataset: dataset from which the original sentence A was extracted (FLICKR vs. SEMEVAL)
- sentence_B_dataset: dataset from which the original sentence B was extracted (FLICKR vs. SEMEVAL) ### Data Splits

Train Trial Test
4439 495 4906 ## Dataset Creation

The dataset was created by first automatically translating all sentences, then by manually correcting any translation errors. This guarantees naturality of the examples while aligning the relatedness scores and entailment labels. Since the data IDs are preserved the dataset is fully aligned on the sentence level. ## Additional Information ### Licensing Information

This dataset falls under an MIT License. ### Contributions

Thanks to @maximedb for adding this dataset.
passage: TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-Dutch #license-mit #region-us 
## Dataset Description

- Homepage: URL
- Repository: URL
- Paper: URL
- Point of Contact: Gijs Wijnholds### Dataset Summary

An automatically translated, manually corrected translation of the SICK dataset of Marelli et al. 2014, intended to boost research in Dutch NLP.### Languages

The dataset is in Dutch.## Dataset Structure### Data Fields

- pair_ID: sentence pair ID
- sentence_A: sentence A
- sentence_B: sentence B
- label: textual entailment gold label: entailment (0), neutral (1) or contradiction (2)
- relatedness_score: semantic relatedness gold score (on a 1-5 continuous scale)
- entailment_AB: entailment for the A-B order (A_neutral_B, A_entails_B, or A_contradicts_B)
- entailment_BA: entailment for the B-A order (B_neutral_A, B_entails_A, or B_contradicts_A)
- sentence_A_original: original sentence from which sentence A is derived
- sentence_B_original: original sentence from which sentence B is derived
- sentence_A_dataset: dataset from which the original sentence A was extracted (FLICKR vs. SEMEVAL)
- sentence_B_dataset: dataset from which the original sentence B was extracted (FLICKR vs. SEMEVAL)### Data Splits

Train Trial Test
4439 495 4906## Dataset Creation

The dataset was created by first automatically translating all sentences, then by manually correcting any translation errors. This guarantees naturality of the examples while aligning the relatedness scores and entailment labels. Since the data IDs are preserved the dataset is fully aligned on the sentence level.## Additional Information### Licensing Information

This dataset falls under an MIT License.### Contributions

Thanks to @maximedb for adding this dataset.