Datasets:
Tasks:
Token Classification
Sub-tasks:
named-entity-recognition
Multilinguality:
multilingual
Size Categories:
n<1K
Language Creators:
crowdsourced
Annotations Creators:
machine-generated
Source Datasets:
original
ArXiv:
Tags:
License:
Update files from the datasets library (from 1.11.0)
Browse filesRelease notes: https://github.com/huggingface/datasets/releases/tag/1.11.0
- README.md +383 -14
- dataset_infos.json +0 -0
- wikiann.py +1 -2
README.md
CHANGED
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@@ -231,25 +231,25 @@ languages:
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| 231 |
os:
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- os
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other-bat-smg:
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-
-
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other-be-x-old:
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-
-
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other-cbk-zam:
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-
-
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other-eml:
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-
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other-fiu-vro:
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-
-
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other-map-bms:
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-
-
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other-simple:
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-
-
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other-zh-classical:
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-
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other-zh-min-nan:
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-
-
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other-zh-yue:
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-
-
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pa:
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- pa
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pdc:
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@@ -369,6 +369,7 @@ task_categories:
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task_ids:
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- named-entity-recognition
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paperswithcode_id: wikiann-1
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| 372 |
---
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| 373 |
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# Dataset Card for WikiANN
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@@ -403,7 +404,7 @@ paperswithcode_id: wikiann-1
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| 403 |
- **Repository:** [Massively Multilingual Transfer for NER](https://github.com/afshinrahimi/mmner)
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- **Paper:** The original datasets come from the _Cross-lingual name tagging and linking for 282 languages_ [paper](https://www.aclweb.org/anthology/P17-1178/) by Xiaoman Pan et al. (2018). This version corresponds to the balanced train, dev, and test splits of the original data from the _Massively Multilingual Transfer for NER_ [paper](https://arxiv.org/abs/1902.00193) by Afshin Rahimi et al. (2019).
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- **Leaderboard:**
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| 406 |
-
- **Point of Contact:** [Afshin Rahimi](mailto:afshinrahimi@gmail.com) or [Lewis Tunstall](mailto:lewis.c.tunstall@gmail.com)
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| 408 |
### Dataset Summary
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| 409 |
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@@ -415,13 +416,201 @@ WikiANN (sometimes called PAN-X) is a multilingual named entity recognition data
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### Languages
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-
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| 419 |
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## Dataset Structure
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| 421 |
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### Data Instances
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-
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### Data Fields
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| 427 |
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@@ -432,7 +621,187 @@ WikiANN (sometimes called PAN-X) is a multilingual named entity recognition data
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### Data Splits
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-
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| 437 |
## Dataset Creation
|
| 438 |
|
| 231 |
os:
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| 232 |
- os
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| 233 |
other-bat-smg:
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| 234 |
+
- sgs
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| 235 |
other-be-x-old:
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| 236 |
+
- be-tarask
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| 237 |
other-cbk-zam:
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| 238 |
+
- cbk
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| 239 |
other-eml:
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| 240 |
+
- eml
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| 241 |
other-fiu-vro:
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| 242 |
+
- vro
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other-map-bms:
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| 244 |
+
- jv-x-bms
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other-simple:
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+
- en-basiceng
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other-zh-classical:
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+
- lzh
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other-zh-min-nan:
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| 250 |
+
- nan
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| 251 |
other-zh-yue:
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| 252 |
+
- yue
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| 253 |
pa:
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| 254 |
- pa
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| 255 |
pdc:
|
| 369 |
task_ids:
|
| 370 |
- named-entity-recognition
|
| 371 |
paperswithcode_id: wikiann-1
|
| 372 |
+
pretty_name: WikiANN
|
| 373 |
---
|
| 374 |
|
| 375 |
# Dataset Card for WikiANN
|
| 404 |
- **Repository:** [Massively Multilingual Transfer for NER](https://github.com/afshinrahimi/mmner)
|
| 405 |
- **Paper:** The original datasets come from the _Cross-lingual name tagging and linking for 282 languages_ [paper](https://www.aclweb.org/anthology/P17-1178/) by Xiaoman Pan et al. (2018). This version corresponds to the balanced train, dev, and test splits of the original data from the _Massively Multilingual Transfer for NER_ [paper](https://arxiv.org/abs/1902.00193) by Afshin Rahimi et al. (2019).
|
| 406 |
- **Leaderboard:**
|
| 407 |
+
- **Point of Contact:** [Afshin Rahimi](mailto:afshinrahimi@gmail.com) or [Lewis Tunstall](mailto:lewis.c.tunstall@gmail.com) or [Albert Villanova del Moral](albert@huggingface.co)
|
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| 409 |
### Dataset Summary
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| 410 |
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### Languages
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+
The dataset contains 176 languages, one in each of the configuration subsets. The corresponding BCP 47 language tags
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+
are:
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+
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+
| | Language tag |
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+
|:-------------------|:---------------|
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+
| ace | ace |
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+
| af | af |
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+
| als | als |
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+
| am | am |
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+
| an | an |
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+
| ang | ang |
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+
| ar | ar |
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| 431 |
+
| arc | arc |
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+
| arz | arz |
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| 433 |
+
| as | as |
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| 434 |
+
| ast | ast |
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| 435 |
+
| ay | ay |
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+
| az | az |
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+
| ba | ba |
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+
| bar | bar |
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+
| be | be |
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+
| bg | bg |
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| 441 |
+
| bh | bh |
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| 442 |
+
| bn | bn |
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| 443 |
+
| bo | bo |
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| 444 |
+
| br | br |
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| 445 |
+
| bs | bs |
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| 446 |
+
| ca | ca |
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| 447 |
+
| cdo | cdo |
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| 448 |
+
| ce | ce |
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| 449 |
+
| ceb | ceb |
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| 450 |
+
| ckb | ckb |
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| 451 |
+
| co | co |
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| 452 |
+
| crh | crh |
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| 453 |
+
| cs | cs |
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| 454 |
+
| csb | csb |
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| 455 |
+
| cv | cv |
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| 456 |
+
| cy | cy |
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| 457 |
+
| da | da |
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| 458 |
+
| de | de |
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| 459 |
+
| diq | diq |
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| 460 |
+
| dv | dv |
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| 461 |
+
| el | el |
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| 462 |
+
| en | en |
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| 463 |
+
| eo | eo |
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| 464 |
+
| es | es |
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| 465 |
+
| et | et |
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| 466 |
+
| eu | eu |
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| 467 |
+
| ext | ext |
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| 468 |
+
| fa | fa |
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| 469 |
+
| fi | fi |
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| 470 |
+
| fo | fo |
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| 471 |
+
| fr | fr |
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| 472 |
+
| frr | frr |
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| 473 |
+
| fur | fur |
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| 474 |
+
| fy | fy |
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| 475 |
+
| ga | ga |
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| 476 |
+
| gan | gan |
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| 477 |
+
| gd | gd |
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| 478 |
+
| gl | gl |
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| 479 |
+
| gn | gn |
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| 480 |
+
| gu | gu |
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| 481 |
+
| hak | hak |
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| 482 |
+
| he | he |
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| 483 |
+
| hi | hi |
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| 484 |
+
| hr | hr |
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| 485 |
+
| hsb | hsb |
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| 486 |
+
| hu | hu |
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| 487 |
+
| hy | hy |
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| 488 |
+
| ia | ia |
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| 489 |
+
| id | id |
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| 490 |
+
| ig | ig |
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| 491 |
+
| ilo | ilo |
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| 492 |
+
| io | io |
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| 493 |
+
| is | is |
|
| 494 |
+
| it | it |
|
| 495 |
+
| ja | ja |
|
| 496 |
+
| jbo | jbo |
|
| 497 |
+
| jv | jv |
|
| 498 |
+
| ka | ka |
|
| 499 |
+
| kk | kk |
|
| 500 |
+
| km | km |
|
| 501 |
+
| kn | kn |
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| 502 |
+
| ko | ko |
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| 503 |
+
| ksh | ksh |
|
| 504 |
+
| ku | ku |
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| 505 |
+
| ky | ky |
|
| 506 |
+
| la | la |
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| 507 |
+
| lb | lb |
|
| 508 |
+
| li | li |
|
| 509 |
+
| lij | lij |
|
| 510 |
+
| lmo | lmo |
|
| 511 |
+
| ln | ln |
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| 512 |
+
| lt | lt |
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| 513 |
+
| lv | lv |
|
| 514 |
+
| mg | mg |
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| 515 |
+
| mhr | mhr |
|
| 516 |
+
| mi | mi |
|
| 517 |
+
| min | min |
|
| 518 |
+
| mk | mk |
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| 519 |
+
| ml | ml |
|
| 520 |
+
| mn | mn |
|
| 521 |
+
| mr | mr |
|
| 522 |
+
| ms | ms |
|
| 523 |
+
| mt | mt |
|
| 524 |
+
| mwl | mwl |
|
| 525 |
+
| my | my |
|
| 526 |
+
| mzn | mzn |
|
| 527 |
+
| nap | nap |
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| 528 |
+
| nds | nds |
|
| 529 |
+
| ne | ne |
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| 530 |
+
| nl | nl |
|
| 531 |
+
| nn | nn |
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| 532 |
+
| no | no |
|
| 533 |
+
| nov | nov |
|
| 534 |
+
| oc | oc |
|
| 535 |
+
| or | or |
|
| 536 |
+
| os | os |
|
| 537 |
+
| other-bat-smg | sgs |
|
| 538 |
+
| other-be-x-old | be-tarask |
|
| 539 |
+
| other-cbk-zam | cbk |
|
| 540 |
+
| other-eml | eml |
|
| 541 |
+
| other-fiu-vro | vro |
|
| 542 |
+
| other-map-bms | jv-x-bms |
|
| 543 |
+
| other-simple | en-basiceng |
|
| 544 |
+
| other-zh-classical | lzh |
|
| 545 |
+
| other-zh-min-nan | nan |
|
| 546 |
+
| other-zh-yue | yue |
|
| 547 |
+
| pa | pa |
|
| 548 |
+
| pdc | pdc |
|
| 549 |
+
| pl | pl |
|
| 550 |
+
| pms | pms |
|
| 551 |
+
| pnb | pnb |
|
| 552 |
+
| ps | ps |
|
| 553 |
+
| pt | pt |
|
| 554 |
+
| qu | qu |
|
| 555 |
+
| rm | rm |
|
| 556 |
+
| ro | ro |
|
| 557 |
+
| ru | ru |
|
| 558 |
+
| rw | rw |
|
| 559 |
+
| sa | sa |
|
| 560 |
+
| sah | sah |
|
| 561 |
+
| scn | scn |
|
| 562 |
+
| sco | sco |
|
| 563 |
+
| sd | sd |
|
| 564 |
+
| sh | sh |
|
| 565 |
+
| si | si |
|
| 566 |
+
| sk | sk |
|
| 567 |
+
| sl | sl |
|
| 568 |
+
| so | so |
|
| 569 |
+
| sq | sq |
|
| 570 |
+
| sr | sr |
|
| 571 |
+
| su | su |
|
| 572 |
+
| sv | sv |
|
| 573 |
+
| sw | sw |
|
| 574 |
+
| szl | szl |
|
| 575 |
+
| ta | ta |
|
| 576 |
+
| te | te |
|
| 577 |
+
| tg | tg |
|
| 578 |
+
| th | th |
|
| 579 |
+
| tk | tk |
|
| 580 |
+
| tl | tl |
|
| 581 |
+
| tr | tr |
|
| 582 |
+
| tt | tt |
|
| 583 |
+
| ug | ug |
|
| 584 |
+
| uk | uk |
|
| 585 |
+
| ur | ur |
|
| 586 |
+
| uz | uz |
|
| 587 |
+
| vec | vec |
|
| 588 |
+
| vep | vep |
|
| 589 |
+
| vi | vi |
|
| 590 |
+
| vls | vls |
|
| 591 |
+
| vo | vo |
|
| 592 |
+
| wa | wa |
|
| 593 |
+
| war | war |
|
| 594 |
+
| wuu | wuu |
|
| 595 |
+
| xmf | xmf |
|
| 596 |
+
| yi | yi |
|
| 597 |
+
| yo | yo |
|
| 598 |
+
| zea | zea |
|
| 599 |
+
| zh | zh |
|
| 600 |
|
| 601 |
## Dataset Structure
|
| 602 |
|
| 603 |
### Data Instances
|
| 604 |
|
| 605 |
+
This is an example in the "train" split of the "af" (Afrikaans language) configuration subset:
|
| 606 |
+
```python
|
| 607 |
+
{
|
| 608 |
+
'tokens': ['Sy', 'ander', 'seun', ',', 'Swjatopolk', ',', 'was', 'die', 'resultaat', 'van', '’n', 'buite-egtelike', 'verhouding', '.'],
|
| 609 |
+
'ner_tags': [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0],
|
| 610 |
+
'langs': ['af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af', 'af'],
|
| 611 |
+
'spans': ['PER: Swjatopolk']
|
| 612 |
+
}
|
| 613 |
+
```
|
| 614 |
|
| 615 |
### Data Fields
|
| 616 |
|
| 621 |
|
| 622 |
### Data Splits
|
| 623 |
|
| 624 |
+
For each configuration subset, the data is split into "train", "validation" and "test" sets, each containing the
|
| 625 |
+
following number of examples:
|
| 626 |
+
|
| 627 |
+
| | Train | Validation | Test |
|
| 628 |
+
|:-------------|--------:|-------------:|-------:|
|
| 629 |
+
| ace | 100 | 100 | 100 |
|
| 630 |
+
| af | 5000 | 1000 | 1000 |
|
| 631 |
+
| als | 100 | 100 | 100 |
|
| 632 |
+
| am | 100 | 100 | 100 |
|
| 633 |
+
| an | 1000 | 1000 | 1000 |
|
| 634 |
+
| ang | 100 | 100 | 100 |
|
| 635 |
+
| ar | 20000 | 10000 | 10000 |
|
| 636 |
+
| arc | 100 | 100 | 100 |
|
| 637 |
+
| arz | 100 | 100 | 100 |
|
| 638 |
+
| as | 100 | 100 | 100 |
|
| 639 |
+
| ast | 1000 | 1000 | 1000 |
|
| 640 |
+
| ay | 100 | 100 | 100 |
|
| 641 |
+
| az | 10000 | 1000 | 1000 |
|
| 642 |
+
| ba | 100 | 100 | 100 |
|
| 643 |
+
| bar | 100 | 100 | 100 |
|
| 644 |
+
| bat-smg | 100 | 100 | 100 |
|
| 645 |
+
| be | 15000 | 1000 | 1000 |
|
| 646 |
+
| be-x-old | 5000 | 1000 | 1000 |
|
| 647 |
+
| bg | 20000 | 10000 | 10000 |
|
| 648 |
+
| bh | 100 | 100 | 100 |
|
| 649 |
+
| bn | 10000 | 1000 | 1000 |
|
| 650 |
+
| bo | 100 | 100 | 100 |
|
| 651 |
+
| br | 1000 | 1000 | 1000 |
|
| 652 |
+
| bs | 15000 | 1000 | 1000 |
|
| 653 |
+
| ca | 20000 | 10000 | 10000 |
|
| 654 |
+
| cbk-zam | 100 | 100 | 100 |
|
| 655 |
+
| cdo | 100 | 100 | 100 |
|
| 656 |
+
| ce | 100 | 100 | 100 |
|
| 657 |
+
| ceb | 100 | 100 | 100 |
|
| 658 |
+
| ckb | 1000 | 1000 | 1000 |
|
| 659 |
+
| co | 100 | 100 | 100 |
|
| 660 |
+
| crh | 100 | 100 | 100 |
|
| 661 |
+
| cs | 20000 | 10000 | 10000 |
|
| 662 |
+
| csb | 100 | 100 | 100 |
|
| 663 |
+
| cv | 100 | 100 | 100 |
|
| 664 |
+
| cy | 10000 | 1000 | 1000 |
|
| 665 |
+
| da | 20000 | 10000 | 10000 |
|
| 666 |
+
| de | 20000 | 10000 | 10000 |
|
| 667 |
+
| diq | 100 | 100 | 100 |
|
| 668 |
+
| dv | 100 | 100 | 100 |
|
| 669 |
+
| el | 20000 | 10000 | 10000 |
|
| 670 |
+
| eml | 100 | 100 | 100 |
|
| 671 |
+
| en | 20000 | 10000 | 10000 |
|
| 672 |
+
| eo | 15000 | 10000 | 10000 |
|
| 673 |
+
| es | 20000 | 10000 | 10000 |
|
| 674 |
+
| et | 15000 | 10000 | 10000 |
|
| 675 |
+
| eu | 10000 | 10000 | 10000 |
|
| 676 |
+
| ext | 100 | 100 | 100 |
|
| 677 |
+
| fa | 20000 | 10000 | 10000 |
|
| 678 |
+
| fi | 20000 | 10000 | 10000 |
|
| 679 |
+
| fiu-vro | 100 | 100 | 100 |
|
| 680 |
+
| fo | 100 | 100 | 100 |
|
| 681 |
+
| fr | 20000 | 10000 | 10000 |
|
| 682 |
+
| frr | 100 | 100 | 100 |
|
| 683 |
+
| fur | 100 | 100 | 100 |
|
| 684 |
+
| fy | 1000 | 1000 | 1000 |
|
| 685 |
+
| ga | 1000 | 1000 | 1000 |
|
| 686 |
+
| gan | 100 | 100 | 100 |
|
| 687 |
+
| gd | 100 | 100 | 100 |
|
| 688 |
+
| gl | 15000 | 10000 | 10000 |
|
| 689 |
+
| gn | 100 | 100 | 100 |
|
| 690 |
+
| gu | 100 | 100 | 100 |
|
| 691 |
+
| hak | 100 | 100 | 100 |
|
| 692 |
+
| he | 20000 | 10000 | 10000 |
|
| 693 |
+
| hi | 5000 | 1000 | 1000 |
|
| 694 |
+
| hr | 20000 | 10000 | 10000 |
|
| 695 |
+
| hsb | 100 | 100 | 100 |
|
| 696 |
+
| hu | 20000 | 10000 | 10000 |
|
| 697 |
+
| hy | 15000 | 1000 | 1000 |
|
| 698 |
+
| ia | 100 | 100 | 100 |
|
| 699 |
+
| id | 20000 | 10000 | 10000 |
|
| 700 |
+
| ig | 100 | 100 | 100 |
|
| 701 |
+
| ilo | 100 | 100 | 100 |
|
| 702 |
+
| io | 100 | 100 | 100 |
|
| 703 |
+
| is | 1000 | 1000 | 1000 |
|
| 704 |
+
| it | 20000 | 10000 | 10000 |
|
| 705 |
+
| ja | 20000 | 10000 | 10000 |
|
| 706 |
+
| jbo | 100 | 100 | 100 |
|
| 707 |
+
| jv | 100 | 100 | 100 |
|
| 708 |
+
| ka | 10000 | 10000 | 10000 |
|
| 709 |
+
| kk | 1000 | 1000 | 1000 |
|
| 710 |
+
| km | 100 | 100 | 100 |
|
| 711 |
+
| kn | 100 | 100 | 100 |
|
| 712 |
+
| ko | 20000 | 10000 | 10000 |
|
| 713 |
+
| ksh | 100 | 100 | 100 |
|
| 714 |
+
| ku | 100 | 100 | 100 |
|
| 715 |
+
| ky | 100 | 100 | 100 |
|
| 716 |
+
| la | 5000 | 1000 | 1000 |
|
| 717 |
+
| lb | 5000 | 1000 | 1000 |
|
| 718 |
+
| li | 100 | 100 | 100 |
|
| 719 |
+
| lij | 100 | 100 | 100 |
|
| 720 |
+
| lmo | 100 | 100 | 100 |
|
| 721 |
+
| ln | 100 | 100 | 100 |
|
| 722 |
+
| lt | 10000 | 10000 | 10000 |
|
| 723 |
+
| lv | 10000 | 10000 | 10000 |
|
| 724 |
+
| map-bms | 100 | 100 | 100 |
|
| 725 |
+
| mg | 100 | 100 | 100 |
|
| 726 |
+
| mhr | 100 | 100 | 100 |
|
| 727 |
+
| mi | 100 | 100 | 100 |
|
| 728 |
+
| min | 100 | 100 | 100 |
|
| 729 |
+
| mk | 10000 | 1000 | 1000 |
|
| 730 |
+
| ml | 10000 | 1000 | 1000 |
|
| 731 |
+
| mn | 100 | 100 | 100 |
|
| 732 |
+
| mr | 5000 | 1000 | 1000 |
|
| 733 |
+
| ms | 20000 | 1000 | 1000 |
|
| 734 |
+
| mt | 100 | 100 | 100 |
|
| 735 |
+
| mwl | 100 | 100 | 100 |
|
| 736 |
+
| my | 100 | 100 | 100 |
|
| 737 |
+
| mzn | 100 | 100 | 100 |
|
| 738 |
+
| nap | 100 | 100 | 100 |
|
| 739 |
+
| nds | 100 | 100 | 100 |
|
| 740 |
+
| ne | 100 | 100 | 100 |
|
| 741 |
+
| nl | 20000 | 10000 | 10000 |
|
| 742 |
+
| nn | 20000 | 1000 | 1000 |
|
| 743 |
+
| no | 20000 | 10000 | 10000 |
|
| 744 |
+
| nov | 100 | 100 | 100 |
|
| 745 |
+
| oc | 100 | 100 | 100 |
|
| 746 |
+
| or | 100 | 100 | 100 |
|
| 747 |
+
| os | 100 | 100 | 100 |
|
| 748 |
+
| pa | 100 | 100 | 100 |
|
| 749 |
+
| pdc | 100 | 100 | 100 |
|
| 750 |
+
| pl | 20000 | 10000 | 10000 |
|
| 751 |
+
| pms | 100 | 100 | 100 |
|
| 752 |
+
| pnb | 100 | 100 | 100 |
|
| 753 |
+
| ps | 100 | 100 | 100 |
|
| 754 |
+
| pt | 20000 | 10000 | 10000 |
|
| 755 |
+
| qu | 100 | 100 | 100 |
|
| 756 |
+
| rm | 100 | 100 | 100 |
|
| 757 |
+
| ro | 20000 | 10000 | 10000 |
|
| 758 |
+
| ru | 20000 | 10000 | 10000 |
|
| 759 |
+
| rw | 100 | 100 | 100 |
|
| 760 |
+
| sa | 100 | 100 | 100 |
|
| 761 |
+
| sah | 100 | 100 | 100 |
|
| 762 |
+
| scn | 100 | 100 | 100 |
|
| 763 |
+
| sco | 100 | 100 | 100 |
|
| 764 |
+
| sd | 100 | 100 | 100 |
|
| 765 |
+
| sh | 20000 | 10000 | 10000 |
|
| 766 |
+
| si | 100 | 100 | 100 |
|
| 767 |
+
| simple | 20000 | 1000 | 1000 |
|
| 768 |
+
| sk | 20000 | 10000 | 10000 |
|
| 769 |
+
| sl | 15000 | 10000 | 10000 |
|
| 770 |
+
| so | 100 | 100 | 100 |
|
| 771 |
+
| sq | 5000 | 1000 | 1000 |
|
| 772 |
+
| sr | 20000 | 10000 | 10000 |
|
| 773 |
+
| su | 100 | 100 | 100 |
|
| 774 |
+
| sv | 20000 | 10000 | 10000 |
|
| 775 |
+
| sw | 1000 | 1000 | 1000 |
|
| 776 |
+
| szl | 100 | 100 | 100 |
|
| 777 |
+
| ta | 15000 | 1000 | 1000 |
|
| 778 |
+
| te | 1000 | 1000 | 1000 |
|
| 779 |
+
| tg | 100 | 100 | 100 |
|
| 780 |
+
| th | 20000 | 10000 | 10000 |
|
| 781 |
+
| tk | 100 | 100 | 100 |
|
| 782 |
+
| tl | 10000 | 1000 | 1000 |
|
| 783 |
+
| tr | 20000 | 10000 | 10000 |
|
| 784 |
+
| tt | 1000 | 1000 | 1000 |
|
| 785 |
+
| ug | 100 | 100 | 100 |
|
| 786 |
+
| uk | 20000 | 10000 | 10000 |
|
| 787 |
+
| ur | 20000 | 1000 | 1000 |
|
| 788 |
+
| uz | 1000 | 1000 | 1000 |
|
| 789 |
+
| vec | 100 | 100 | 100 |
|
| 790 |
+
| vep | 100 | 100 | 100 |
|
| 791 |
+
| vi | 20000 | 10000 | 10000 |
|
| 792 |
+
| vls | 100 | 100 | 100 |
|
| 793 |
+
| vo | 100 | 100 | 100 |
|
| 794 |
+
| wa | 100 | 100 | 100 |
|
| 795 |
+
| war | 100 | 100 | 100 |
|
| 796 |
+
| wuu | 100 | 100 | 100 |
|
| 797 |
+
| xmf | 100 | 100 | 100 |
|
| 798 |
+
| yi | 100 | 100 | 100 |
|
| 799 |
+
| yo | 100 | 100 | 100 |
|
| 800 |
+
| zea | 100 | 100 | 100 |
|
| 801 |
+
| zh | 20000 | 10000 | 10000 |
|
| 802 |
+
| zh-classical | 100 | 100 | 100 |
|
| 803 |
+
| zh-min-nan | 100 | 100 | 100 |
|
| 804 |
+
| zh-yue | 20000 | 10000 | 10000 |
|
| 805 |
|
| 806 |
## Dataset Creation
|
| 807 |
|
dataset_infos.json
CHANGED
|
The diff for this file is too large to render.
See raw diff
|
wikiann.py
CHANGED
|
@@ -42,8 +42,7 @@ _CITATION = """@inproceedings{pan-etal-2017-cross,
|
|
| 42 |
|
| 43 |
_DESCRIPTION = """WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles annotated with LOC (location), PER (person), and ORG (organisation) tags in the IOB2 format. This version corresponds to the balanced train, dev, and test splits of Rahimi et al. (2019), which supports 176 of the 282 languages from the original WikiANN corpus."""
|
| 44 |
|
| 45 |
-
|
| 46 |
-
_DATA_URL = "https://www.dropbox.com/s/12h3qqog6q4bjve/panx_dataset.tar?dl=1"
|
| 47 |
_HOMEPAGE = "https://github.com/afshinrahimi/mmner"
|
| 48 |
_VERSION = "1.1.0"
|
| 49 |
_LANGS = [
|
| 42 |
|
| 43 |
_DESCRIPTION = """WikiANN (sometimes called PAN-X) is a multilingual named entity recognition dataset consisting of Wikipedia articles annotated with LOC (location), PER (person), and ORG (organisation) tags in the IOB2 format. This version corresponds to the balanced train, dev, and test splits of Rahimi et al. (2019), which supports 176 of the 282 languages from the original WikiANN corpus."""
|
| 44 |
|
| 45 |
+
_DATA_URL = "https://s3.amazonaws.com/datasets.huggingface.co/wikiann/1.1.0/panx_dataset.zip"
|
|
|
|
| 46 |
_HOMEPAGE = "https://github.com/afshinrahimi/mmner"
|
| 47 |
_VERSION = "1.1.0"
|
| 48 |
_LANGS = [
|