Datasets:
File size: 12,605 Bytes
20d2b78 3755dcc 20d2b78 3755dcc 20d2b78 3755dcc 5035f15 20d2b78 e1a01a2 5035f15 e1a01a2 5035f15 e1a01a2 5035f15 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
- name: language
dtype:
class_label:
names:
'0': plt_Latn
'1': sun_Latn
'2': ukr_Cyrl
'3': spa_Latn
'4': por_Latn
'5': mya_Mymr
'6': mkd_Cyrl
'7': war_Latn
'8': nso_Latn
'9': wol_Latn
'10': kam_Latn
'11': mal_Mlym
'12': gle_Latn
'13': ayr_Latn
'14': rus_Cyrl
'15': pbt_Arab
'16': pag_Latn
'17': twi_Latn
'18': als_Latn
'19': lit_Latn
'20': amh_Ethi
'21': tur_Latn
'22': tel_Telu
'23': vec_Latn
'24': zsm_Latn
'25': ckb_Arab
'26': tgk_Cyrl
'27': tha_Thai
'28': hye_Armn
'29': deu_Latn
'30': tat_Cyrl
'31': swh_Latn
'32': kac_Latn
'33': tuk_Latn
'34': lvs_Latn
'35': tso_Latn
'36': fao_Latn
'37': tpi_Latn
'38': umb_Latn
'39': mlt_Latn
'40': cym_Latn
'41': ben_Beng
'42': hat_Latn
'43': ron_Latn
'44': tir_Ethi
'45': ewe_Latn
'46': ind_Latn
'47': snd_Arab
'48': nld_Latn
'49': urd_Arab
'50': vie_Latn
'51': mar_Deva
'52': fra_Latn
'53': lug_Latn
'54': pol_Latn
'55': ban_Latn
'56': est_Latn
'57': srp_Cyrl
'58': kin_Latn
'59': nno_Latn
'60': fur_Latn
'61': kmr_Latn
'62': bho_Deva
'63': fin_Latn
'64': mri_Latn
'65': ilo_Latn
'66': fij_Latn
'67': slk_Latn
'68': knc_Arab
'69': guj_Gujr
'70': kor_Hang
'71': tum_Latn
'72': kab_Latn
'73': afr_Latn
'74': eng_Latn
'75': acq_Arab
'76': som_Latn
'77': tgl_Latn
'78': epo_Latn
'79': bjn_Arab
'80': mni_Beng
'81': sot_Latn
'82': nob_Latn
'83': kat_Geor
'84': ory_Orya
'85': arb_Arab
'86': heb_Hebr
'87': ibo_Latn
'88': asm_Beng
'89': uzn_Latn
'90': sna_Latn
'91': mos_Latn
'92': fuv_Latn
'93': hne_Deva
'94': apc_Arab
'95': hun_Latn
'96': ita_Latn
'97': bem_Latn
'98': slv_Latn
'99': ssw_Latn
'100': szl_Latn
'101': nya_Latn
'102': kir_Cyrl
'103': hrv_Latn
'104': pap_Latn
'105': kik_Latn
'106': knc_Latn
'107': lmo_Latn
'108': hau_Latn
'109': eus_Latn
'110': ltz_Latn
'111': grn_Latn
'112': lus_Latn
'113': taq_Latn
'114': scn_Latn
'115': kmb_Latn
'116': azj_Latn
'117': isl_Latn
'118': swe_Latn
'119': uig_Arab
'120': jpn_Jpan
'121': sag_Latn
'122': xho_Latn
'123': ast_Latn
'124': kan_Knda
'125': sin_Sinh
'126': acm_Arab
'127': tzm_Tfng
'128': dan_Latn
'129': zho_Hant
'130': zho_Hans
'131': pes_Arab
'132': fon_Latn
'133': tam_Taml
'134': yor_Latn
'135': run_Latn
'136': arz_Arab
'137': awa_Deva
'138': pan_Guru
'139': gaz_Latn
'140': lao_Laoo
'141': bos_Latn
'142': ces_Latn
'143': bam_Latn
'144': crh_Latn
'145': ltg_Latn
'146': bul_Cyrl
'147': gla_Latn
'148': ell_Grek
'149': prs_Arab
'150': smo_Latn
'151': ajp_Arab
'152': tsn_Latn
'153': bak_Cyrl
'154': srd_Latn
'155': ace_Arab
'156': kas_Arab
'157': lua_Latn
'158': taq_Tfng
'159': jav_Latn
'160': cat_Latn
'161': kon_Latn
'162': hin_Deva
'163': lin_Latn
'164': khk_Cyrl
'165': cjk_Latn
'166': mag_Deva
'167': dik_Latn
'168': bug_Latn
'169': bjn_Latn
'170': yue_Hant
'171': zul_Latn
'172': npi_Deva
'173': kas_Deva
'174': dzo_Tibt
'175': ary_Arab
'176': bel_Cyrl
'177': kbp_Latn
'178': khm_Khmr
'179': ace_Latn
'180': nus_Latn
'181': ceb_Latn
'182': mai_Deva
'183': san_Deva
'184': dyu_Latn
'185': quy_Latn
'186': lim_Latn
'187': min_Latn
'188': oci_Latn
'189': kaz_Cyrl
'190': luo_Latn
'191': sat_Olck
'192': ydd_Hebr
'193': shn_Mymr
'194': ars_Arab
'195': lij_Latn
'196': aeb_Arab
'197': bod_Tibt
'198': glg_Latn
'199': kea_Latn
'200': azb_Arab
- name: dataset_source
dtype: string
splits:
- name: train
num_bytes: 21749592609
num_examples: 118296182
download_size: 16568412828
dataset_size: 21749592609
license: other
task_categories:
- text-classification
size_categories:
- 100M<n<1B
---
# Dataset Card for "open-lid-dataset"
## Dataset Description
- **Repository:** [https://github.com/laurieburchell/open-lid-dataset]()
- **Paper:** [An Open Dataset and Model for Language Identification](https://aclanthology.org/2023.acl-short.75/)
- **Point of Contact:** laurie.burchell AT ed.ac.uk
### Dataset Summary
The OpenLID dataset covers 201 languages and is designed for training language identification models. The majority of the source datasets were derived from news sites, Wikipedia, or religious text, though some come from other domains (e.g. transcribed conversations, literature, or social media). A sample of each language in each source was manually audited to check it was in the attested language (see [the paper](https://aclanthology.org/2023.acl-short.75/)) for full details.
### Supported tasks
This dataset is intended for training high-coverage language identification models (e.g. [OpenLID](https://huggingface.co/laurievb/OpenLID)). It is compatible with the [FLORES-200](https://github.com/facebookresearch/flores/tree/main/flores200) evaluation benchmark.
### Languages
There are 201 languages included in the dataset with varying amounts of data: the largest class (English) contains 7.5 million lines of data, and the smallest (South Azerbaijani) contains 532 lines of data. The mean number of lines per language is 602,812. A full breakdown of lines of data per language is available [on the repo](https://github.com/laurieburchell/open-lid-dataset/blob/main/languages.md).
## Dataset Structure
### Data Instances
Each entry in the dataset consists of a line of data, a language label included script information, and a tag indicating the source.
```json
{
"text": "¿Serás exaltada hasta el cielo?",
"language": "spa_Latn",
"dataset_source": "lti"
}
```
### Data Splits
Only a train split is provided. The dataset is designed to be compatible with the [FLORES-200](https://github.com/facebookresearch/flores/tree/main/flores200) evaluation benchmark.
## Dataset Creation
### Curation Rationale
Recent work has found that existing language identification algorithms perform poorly in practice compared to test performance. The problem is particularly acute for low-resource languages: [Kreutzer et al. (2022)](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00447/109285/Quality-at-a-Glance-An-Audit-of-Web-Crawled) found a positive Spearman rank correlation between quality of data and size of language for all of the \ac{lid}-filtered multilingual datasets they studied. In addition, for a significant fraction of the language corpora they studied, less than half of the sentences were in the correct language. They point out that such low-quality data not only leads to poor performance in downstream tasks, but that it also contributes to `representation washing', where the community is given a false view of the actual progress of low-resource natural language processing.
There are several open language identification models offering quick classification and high language coverage (e.g. CLD3, No Language Left Behind). However, to the best of our knowledge, none of the commonly-used scalable language identificaiton systems make their training data public.
This dataset aims to address that gap by curating and combining sources of open training data for language identification and by auditing a sample of all languages in each source to check reliability.
### Source Data
The majority of the source datasets were derived from news sites, Wikipedia, or religious text, though some come from other domains (e.g. transcribed conversations, literature, or social media). We provide a full list at the end of this model card along with the licensing information for each source.
#### Initial Data Collection and Normalisation
Our initial aim was to cover the same languages present in the FLORES-200 Evaluation Benchmark so that we could use this dataset for evaluation. However, during the curation process, we decided to exclude three languages. Firstly, though Akan and Twi are both included as separate languages in FLORES-200, Akan is actually a macrolanguage covering a language continuum which includes Twi. Given the other languages in FLORES-200 are individual languages, we decided to exclude Akan. Secondly, FLORES-200 includes Modern Standard Arabic (MSA) written in Latin script. It is true that Arabic dialects are often written in Latin characters in informal situations (e.g. social media). However, MSA is a form of standardised Arabic which is not usually used in informal situations. Since we could not any find naturally-occurring training data, we excluded MSA from the dataset. Finally, we excluded Minangkabau in Arabic script because it is now rarely written this way, making it difficult to find useful training data.
The first step in our manual audit was to check and standardise language labels, as these are often inconsistent or idiosyncratic. We chose to copy the language codes in FLORES-200 and reassign macrolanguage or ambiguous language codes in the data sources we found to the dominant individual language. Whilst this resulted in more useful data for some languages, for other languages we had to be more conservative. For example, we originally reassigned text labelled as the macrolanguage Malay (msa_Latn) to Standard Malay, but this led to a large drop in performance as the former covers a very diverse set of languages.
Two of the authors then carried out a manual audit of a random sample of all data sources and languages: one a native Bulgarian speaker (able to read Cyrillic and Latin scripts and Chinese characters), and the other a native English speaker (able to read Latin, Arabic and Hebrew scripts). For languages we knew, we checked the language was what we expected. For unfamiliar languages in a script we could read, we compared the sample to the Universal Declaration of Human Rights or failing that, to a sample of text on Wikipedia. We compared features of the text which are common in previous language identification algorithms and could be identified easily by humans: similar diacritics, word lengths, common words, loan words matching the right cultural background, similar suffixes and prefixes, and vowel/consonant patterns. For scripts we could not read, we checked that all lines of the sample matched the script in the Universal Declaration of Human Rights.
We kept preprocessing minimal so that the process was as language agnostic as possible. We used the scripts provided with Moses to remove non-printing characters and detokenise the data where necessary. We then filtered the data so that each line contained at least one character in the expected script (as defined by Perl) to allow for borrowings. Finally, we sampled proportionally to $ p_l^{0.3} $, where $ p_l $ is the fraction of lines in the dataset which are in language $ l $. This aims to ameliorate class skew issues.
## Considerations for Using the Data
### Social Impact of Dataset |