Create README.md

README.md

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+---
+language:
+- en
+pretty_name: CANNOT
+---
+# Dataset Card for CANNOT
+
+## Dataset Description
+
+- **Homepage: https://github.com/dmlls/cannot-dataset** 
+- **Repository: https://github.com/dmlls/cannot-dataset** 
+- **Paper: tba**
+
+### Dataset Summary
+
+
+**CANNOT** is a dataset that focuses on negated textual pairs. It currently
+contains **77,376 samples**, of which roughly of them are negated pairs of
+sentences, and the other half are not (they are paraphrased versions of each
+other).
+
+The most frequent negation that appears in the dataset is verbal negation (e.g.,
+will → won't), although it also contains pairs with antonyms (cold → hot).
+
+### Languages
+CANNOT includes exclusively texts in **English**.
+
+## Dataset Structure
+
+The dataset is given as a
+[`.tsv`](https://en.wikipedia.org/wiki/Tab-separated_values) file with the
+following structure:
+
+| premise     | hypothesis                                         | label |
+|:------------|:---------------------------------------------------|:-----:|
+| A sentence. | An equivalent, non-negated sentence (paraphrased). | 0     |
+| A sentence. | The sentence negated.                              | 1     |
+
+
+The dataset can be easily loaded into a Pandas DataFrame by running:
+
+```Python
+import pandas as pd
+
+dataset = pd.read_csv('negation_dataset_v1.0.tsv', sep='\t')
+
+```
+
+## Dataset Creation
+
+
+The dataset has been created by cleaning up and merging the following datasets:
+
+1. _Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal
+Negation_ (see
+[`datasets/nan-nli`](https://github.com/dmlls/cannot-dataset/tree/main/datasets/nan-nli)).
+
+2. _GLUE Diagnostic Dataset_ (see
+[`datasets/glue-diagnostic`](https://github.com/dmlls/cannot-dataset/tree/main/datasets/glue-diagnostic)).
+
+3. _Automated Fact-Checking of Claims from Wikipedia_ (see
+[`datasets/wikifactcheck-english`](https://github.com/dmlls/cannot-dataset/tree/main/datasets/wikifactcheck-english)).
+
+4. _From Group to Individual Labels Using Deep Features_ (see
+[`datasets/sentiment-labelled-sentences`](https://github.com/dmlls/cannot-dataset/tree/main/datasets/sentiment-labelled-sentences)).
+In this case, the negated sentences were obtained by using the Python module
+[`negate`](https://github.com/dmlls/negate).
+
+
+Additionally, for each of the negated samples, another pair of non-negated
+sentences has been added by paraphrasing them with the pre-trained model
+[`🤗tuner007/pegasus_paraphrase`](https://huggingface.co/tuner007/pegasus_paraphrase).
+
+Furthermore, the dataset from _It Is Not Easy To Detect Paraphrases: Analysing
+Semantic Similarity With Antonyms and Negation Using the New SemAntoNeg
+Benchmark_ (see
+[`datasets/antonym-substitution`](https://github.com/dmlls/cannot-dataset/tree/main/datasets/antonym-substitution))
+has also been included. This dataset already provides both the paraphrased and
+negated version for each premise, so no further processing was needed.
+
+Finally, the swapped version of each pair (premise ⇋ hypothesis) has also been
+included, and any duplicates have been removed.
+
+The contribution of each of these individual datasets to the final CANNOT
+dataset is:
+
+| Dataset                                                                   | Samples    |
+|:--------------------------------------------------------------------------|-----------:|
+| Not another Negation Benchmark                                            |      118   |
+| GLUE Diagnostic Dataset                                                   |      154   |
+| Automated Fact-Checking of Claims from Wikipedia                          |   14,970   |
+| From Group to Individual Labels Using Deep Features                       |    2,110   |
+| It Is Not Easy To Detect Paraphrases                                      |    8,597   |
+| <p align="right"><b>Total</b></p>                                         | **25,949** |
+
+_Note_: The numbers above include only the original queries present in the
+datasets.
+
+
+## Additional Information
+
+### Licensing Information
+
+TODO
+
+### Citation Information
+
+tba
+
+### Contributions
+
+Contributions to the dataset can be submitted through the [project repository](https://github.com/dmlls/cannot-dataset).