Update README.md

An evaluation set for monotonicity reasoning as (binary) Natural Language Inference for Dutch.
The dataset is a human-verified translation from the MED dataset of (Yanaka et al., 2019).

Each example is a pair of sentences, with a label indicating Entailment or Non-Entailment, making it a binary two-sentence classification task.
Each example is additionally labelled with its original source, monotonicity context (upward, downward, non) and linguistic phenomena being evaluated (e.g. lexical knowledge, disjunction, negation).

Please cite the below article if you use this dataset in your work.

```
@inproceedings{wijnholds-2023-assessing,
title = "Assessing Monotonicity Reasoning in {D}utch through Natural Language Inference",
author = "Wijnholds, Gijs",
booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
month = may,
year = "2023",
address = "Dubrovnik, Croatia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-eacl.110",
pages = "1464--1470",
abstract = "In this paper we investigate monotonicity reasoning in Dutch, through a novel Natural Language Inference dataset. Monotonicity reasoning shows to be highly challenging for Transformer-based language models in English and here, we corroborate those findings using a parallel Dutch dataset, obtained by translating the Monotonicity Entailment Dataset of Yanaka et al. (2019). After fine-tuning two Dutch language models BERTje and RobBERT on the Dutch NLI dataset SICK-NL, we find that performance severely drops on the monotonicity reasoning dataset, indicating poor generalization capacity of the models. We provide a detailed analysis of the test results by means of the linguistic annotations in the dataset. We find that models struggle with downward entailing contexts, and argue that this is due to a poor understanding of negation. Additionally, we find that the choice of monotonicity context affects model performance on conjunction and disjunction. We hope that this new resource paves the way for further research in generalization of neural reasoning models in Dutch, and contributes to the development of better language technology for Natural Language Inference, specifically for Dutch.",
}
```

README.md

@@ -1,3 +1,13 @@
 ---
 license: cc-by-nc-sa-4.0
----
+task_categories:
+- text-classification
+language:
+- nl
+tags:
+- monotonicity
+- natural language inference
+pretty_name: MED-NL
+size_categories:
+- 1K<n<10K
+---