Datasets:
File size: 5,037 Bytes
83a93e0 1212be3 8f22be0 83a93e0 496c6b6 83a93e0 496c6b6 eae5149 c0a6179 83a93e0 496c6b6 eae5149 c0a6179 83a93e0 5b480e7 ec85bb6 5b480e7 ec85bb6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 |
---
language:
- da
- nb
- nn
- sv
dataset_info:
- config_name: Danish
features:
- name: text
dtype: string
- name: corruption_type
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 139194
num_examples: 1024
- name: test
num_bytes: 281517
num_examples: 2048
- name: full_train
num_bytes: 733506
num_examples: 5342
- name: val
num_bytes: 32942
num_examples: 256
download_size: 700593
dataset_size: 1187159
- config_name: Norwegian_b
features:
- name: text
dtype: string
- name: corruption_type
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 126028
num_examples: 1024
- name: test
num_bytes: 258103
num_examples: 2048
- name: full_train
num_bytes: 3221649
num_examples: 25946
- name: val
num_bytes: 31302
num_examples: 256
download_size: 2161548
dataset_size: 3637082
- config_name: Norwegian_n
features:
- name: text
dtype: string
- name: corruption_type
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 136251
num_examples: 1024
- name: test
num_bytes: 268761
num_examples: 2048
- name: full_train
num_bytes: 3062138
num_examples: 22800
- name: val
num_bytes: 33910
num_examples: 256
download_size: 2088966
dataset_size: 3501060
- config_name: Swedish
features:
- name: text
dtype: string
- name: corruption_type
dtype: string
- name: label
dtype: string
splits:
- name: train
num_bytes: 135999
num_examples: 1024
- name: test
num_bytes: 262897
num_examples: 2048
- name: full_train
num_bytes: 1014513
num_examples: 7446
- name: val
num_bytes: 36681
num_examples: 256
download_size: 807624
dataset_size: 1450090
configs:
- config_name: Danish
data_files:
- split: train
path: Danish/train-*
- split: test
path: Danish/test-*
- split: full_train
path: Danish/full_train-*
- split: val
path: Danish/val-*
- config_name: Norwegian_b
data_files:
- split: train
path: Norwegian_b/train-*
- split: test
path: Norwegian_b/test-*
- split: full_train
path: Norwegian_b/full_train-*
- split: val
path: Norwegian_b/val-*
- config_name: Norwegian_n
data_files:
- split: train
path: Norwegian_n/train-*
- split: test
path: Norwegian_n/test-*
- split: full_train
path: Norwegian_n/full_train-*
- split: val
path: Norwegian_n/val-*
- config_name: Swedish
data_files:
- split: train
path: Swedish/train-*
- split: test
path: Swedish/test-*
- split: full_train
path: Swedish/full_train-*
- split: val
path: Swedish/val-*
---
## ScandEval
Multilingual version of nordic languages dataset for linguistic acceptability classification.
See versions for:
- Swedish: https://huggingface.co/datasets/mteb/scala_sv_classification
- Norwegian Bokmål: https://huggingface.co/datasets/mteb/scala_nn_classification
- Norwegian Nynorsk: https://huggingface.co/datasets/mteb/scala_nb_classification
- Danish: https://huggingface.co/datasets/mteb/scala_da_classification
Reference: https://aclanthology.org/2023.nodalida-1.20/
Cite:
```
@inproceedings{nielsen-2023-scandeval,
title = "{S}cand{E}val: A Benchmark for {S}candinavian Natural Language Processing",
author = "Nielsen, Dan",
editor = {Alum{\"a}e, Tanel and
Fishel, Mark},
booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = may,
year = "2023",
address = "T{\'o}rshavn, Faroe Islands",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2023.nodalida-1.20",
pages = "185--201",
abstract = "This paper introduces a Scandinavian benchmarking platform, ScandEval, which can benchmark any pretrained model on four different tasks in the Scandinavian languages. The datasets used in two of the tasks, linguistic acceptability and question answering, are new. We develop and release a Python package and command-line interface, scandeval, which can benchmark any model that has been uploaded to the Hugging Face Hub, with reproducible results. Using this package, we benchmark more than 80 Scandinavian or multilingual models and present the results of these in an interactive online leaderboard, as well as provide an analysis of the results. The analysis shows that there is substantial cross-lingual transfer among the the Mainland Scandinavian languages (Danish, Swedish and Norwegian), with limited cross-lingual transfer between the group of Mainland Scandinavian languages and the group of Insular Scandinavian languages (Icelandic and Faroese). The benchmarking results also show that the investment in language technology in Norway and Sweden has led to language models that outperform massively multilingual models such as XLM-RoBERTa and mDeBERTaV3. We release the source code for both the package and leaderboard.",
}
``` |