Update README.md

README.md

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+---
+
+# DaLA: Danish Linguistic Acceptability Evaluation Dataset
+
+**NOTE: This is a variant of [DaLA Standard](https://huggingface.co/datasets/giannor/dala) with labels in Danish language instead of English (as in the original one), the data is the same. The following information are the same contained in the original repository**
+
+---
+
+**DaLA** ([paper][1]) is a benchmark dataset for **linguistic acceptability judgment** in Danish, designed to evaluate how well NLP models, especially large language models (LLMs), understand grammaticality in real-world Danish sentences. The dataset extends previous resources by introducing a broader and more realistic set of error types and providing data splits suitable for evaluation via few-shot or finetuning.
+
+---
+
+## 🔗 Links
+- DaLA variants are linked and described below
+- [Paper][1]
+- [GitHub Repository](https://github.com/N-essuno/DaLA) (code, data generation scripts)
+
+---
+
+## 📖 Overview
+
+In linguistic acceptability tasks, models must distinguish between **grammatically acceptable** and **unacceptable** sentences. The DaLA dataset was created by:
+
+- Analyzing real-world Danish writing errors.
+- Designing **14 distinct corruption functions** that reflect common Danish mistakes (e.g., pronoun confusion, suffix errors, interchange of determiners).
+- Applying a single corruption to each correct Danish sentence creating an incorrect counterpart, resulting in **minimal pairs** of sentences that differ by only one error.
+
+The dataset includes:
+- The original correct sentences (*acceptable*).
+- The corrupted sentences (*unacceptable*).
+- A binary acceptability label.
+- A corruption type identifier.
+
+---
+
+## 📦 Dataset Variants and Splits
+
+There are three variants of the DaLA dataset, each with different sizes and proportions:
+
+| Split Variant | Description | Size (approx.) | Link |
+|------------------|-------------|----------------|----------------|
+| `dala` | Standard benchmark with proportions comparable to prior Danish acceptability datasets | 3,328 samples | [DaLA Standard](https://huggingface.co/datasets/giannor/dala) |
+| `dala_medium` | Expanded version using more available samples | ~6,056 samples | [DaLA Medium](https://huggingface.co/datasets/giannor/dala_medium) |
+| `dala_large` | Largest version with the full expanded dataset | ~7,656 samples | [DaLA Large](https://huggingface.co/datasets/giannor/dala_large) |
+
+Each variant includes train, validation, and test splits.
+
+---
+
+## 🧠 Tasks & Usage
+
+DaLA is primarily intended for:
+
+✔ **Model evaluation and benchmarking**: Assessing model competence in grammatical judgment
+✔ **Minimal-pair evaluation**: Error type discrimination and fine-grained analysis
+
+You can load the dataset using the Hugging Face `datasets` library as follows:
+
+```python
+from datasets import load_dataset
+
+# Standard split
+dataset = load_dataset("giannor/dala")
+
+# Medium or large variants
+dataset_medium = load_dataset("giannor/dala_medium")
+dataset_large = load_dataset("giannor/dala_large")
+```
+
+## 📊 Baselines & Model Performance
+
+In the corresponding paper, DaLA was used to benchmark a variety of open-source LLMs and model types. Across many models, performance on DaLA was **lower** than on previous Danish acceptability benchmarks, highlighting DaLA’s **greater difficulty and discriminatory power**. ([DaLA paper][1])
+
+---
+
+## 📄 Citation
+
+If you use this dataset in your work, please cite the following paper:
+
+```bibtex
+
+
+@misc
+{barmina2025daladanishlinguisticacceptability,
+title={DaLA: Danish Linguistic Acceptability Evaluation Guided by Real World Errors},
+author={Gianluca Barmina and Nathalie Carmen Hau Norman and Peter Schneider-Kamp and Lukas Galke},
+year={2025},
+eprint={2512.04799},
+archivePrefix={arXiv},
+primaryClass={cs.CL},
+url={https://arxiv.org/abs/2512.04799},
+}
+```
+
+---
+
+## ⚖️ License
+
+This dataset is shared under the **CC BY 4.0** license.
+
+
+[1]: https://arxiv.org/abs/2512.04799 "DaLA: Danish Linguistic Acceptability Evaluation Guided by Real World Errors"