# Icelandic WinoGrande ### Paper Title: `A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models` Link: https://aclanthology.org/2022.lrec-1.464/ Dataset: https://huggingface.co/datasets/mideind/icelandic-winogrande Icelandic WinoGrande is a manually translated and localized version of the English-language WinoGrande dataset, designed to be 'a new and challenging benchmark for commonsense reasoning and natural language understanding' in Icelandic [(Snæbjarnarson et al., 2022)](https://aclanthology.org/2022.lrec-1.464/). **Implementation Note:** The original dataset is designed for evaluation on a BERT model. Following the evaluation method used for the original (English-language) WinoGrande on the Harness (see information [here](../winogrande/README.md)), this evaluation uses partial scoring as described by [Trinh & Le (2018)](https://arxiv.org/abs/1806.02847) to allow evaluation on autoregressive models. ### Groups and Tasks #### Groups * Not part of a group yet. #### Tasks * `icelandic_winogrande` ### Citation ``` @inproceedings{snaebjarnarson-etal-2022-warm, title = "A Warm Start and a Clean Crawled Corpus - A Recipe for Good Language Models", author = "Sn{\ae}bjarnarson, V{\'e}steinn and S{\'i}monarson, Haukur Barri and Ragnarsson, P{\'e}tur Orri and Ing{\'o}lfsd{\'o}ttir, Svanhv{\'i}t Lilja and J{\'o}nsson, Haukur and Thorsteinsson, Vilhjalmur and Einarsson, Hafsteinn", editor = "Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Odijk, Jan and Piperidis, Stelios", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.464/", pages = "4356--4366" } ``` ### Checklist For adding novel benchmarks/datasets to the library: * [x] Is the task an existing benchmark in the literature? * [x] Have you referenced the original paper that introduced the task? * [x] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?