The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 141 new columns ({'tamil', 'sme', 'uk', 'orv', 'nn', 'lv', 'be', 'thai', 'af', 'gl', 'sk', 'turkish', 'ancient_greek', 'indonesian', 'livvi', 'scottish_gaelic', 'es', 'norwegian_bokmaal', 'lzh', 'id', 'classical_chinese', 'italian', 'spanish', 'coptic', 'bulgarian', 'he', 'ko', 'czech', 'fa', 'fro', 'dutch', 'ga', 'cu', 'north_sami', 'ar', 'cop', 'tr', 'french', 'mr', 'kmr', 'zh-hans', 'buryat', 'kurmanji', 'vi', 'zh', 'wolof', 'gothic', 'eu', 'hy', 'upper_sorbian', 'slovenian', 'hindi', 'nb', 'ca', 'olo', 'old_russian', 'sv', 'telugu', 'fr', 'serbian', 'hebrew', 'lithuanian', 'galician', 'irish', 'ru', 'belarusian', 'simplified_chinese', 'latvian', 'it', 'en', 'et', 'bxr', 'th', 'ug', 'ro', 'el', 'fi', 'te', 'latin', 'danish', 'croatian', 'pl', 'swl', 'catalan', 'cs', 'armenian', 'la', 'marathi', 'uyghur', 'got', 'hsb', 'arabic', 'da', 'greek', 'ta', 'russian', 'wo', 'finnish', 'zh-hant', 'ja', 'estonian', 'lt', 'mt', 'vietnamese', 'sl', 'traditional_chinese', 'norwegian_nynorsk', 'romanian', 'german', 'basque', 'sr', 'swedish', 'ur', 'hi', 'persian', 'old_french', 'maltese', 'kk', 'portuguese', 'no', 'japanese', 'pt', 'english', 'nl', 'hungarian', 'bg', 'old_church_slavonic', 'urdu', 'grc', 'slovak', 'kazakh', 'hu', 'polish', 'afrikaans', 'korean', 'swedish_sign_language', 'gd', 'ukrainian', 'url', 'de', 'hr'}) and 6 missing columns ({'to_name', 'to_code', 'from_code', 'from_name', 'package_version', 'argos_version'}).

This happened while the json dataset builder was generating data using

hf://datasets/GeorgeSpark/translationmodels/en_ja/stanza/resources.json (at revision 601abf1a8a103e0f46a1f3761d22bfa71540909c)

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              ar: struct<backward_charlm: struct<ccwiki: struct<md5: string>>, forward_charlm: struct<ccwiki: struct<md5: string>>, depparse: struct<padt: struct<md5: string, dependencies: list<item: struct<model: string, package: string>>>>, lemma: struct<padt: struct<md5: string>>, mwt: struct<padt: struct<md5: string>>, ner: struct<aqmar: struct<md5: string, dependencies: list<item: struct<model: string, package: string>>>>, pos: struct<padt: struct<md5: string, dependencies: list<item: struct<model: string, package: string>>>>, pretrain: struct<padt: struct<md5: string>>, tokenize: struct<padt: struct<md5: string>>, default_processors: struct<tokenize: string, mwt: string, lemma: string, pos: string, depparse: string, ner: string>, default_dependencies: struct<pos: list<item: struct<model: string, package: string>>, depparse: list<item: struct<model: string, package: string>>, ner: list<item: struct<model: string, package: string>>>, default_md5: string, lang_name: string>
                child 0, backward_charlm: struct<ccwiki: struct<md5: string>>
                    child 0, ccwiki: struct<md5: string>
                        child 0, md5: string
                child 1, forward_charlm: struct<ccwiki: struct<md5: string>>
                    child 0, ccwiki: struct<md5: string>
                        child 0, md5: string
                child 2, depparse: struct<padt: struct<md5: string, dependencies: list<item: struct<model: string, package: string>>>>
                    child 0, padt: struct<md5: string, dependencies: list<item: struct<model: string, package: string>>>
                        child 0, 
              ...
              st<item: struct<model: string, package: string>>
                            child 0, item: struct<model: string, package: string>
                                child 0, model: string
                                child 1, package: string
                child 3, pretrain: struct<gsd: struct<md5: string>>
                    child 0, gsd: struct<md5: string>
                        child 0, md5: string
                child 4, tokenize: struct<gsd: struct<md5: string>>
                    child 0, gsd: struct<md5: string>
                        child 0, md5: string
                child 5, default_processors: struct<tokenize: string, lemma: string, pos: string, depparse: string>
                    child 0, tokenize: string
                    child 1, lemma: string
                    child 2, pos: string
                    child 3, depparse: string
                child 6, default_dependencies: struct<pos: list<item: struct<model: string, package: string>>, depparse: list<item: struct<model: string, package: string>>>
                    child 0, pos: list<item: struct<model: string, package: string>>
                        child 0, item: struct<model: string, package: string>
                            child 0, model: string
                            child 1, package: string
                    child 1, depparse: list<item: struct<model: string, package: string>>
                        child 0, item: struct<model: string, package: string>
                            child 0, model: string
                            child 1, package: string
                child 7, default_md5: string
                child 8, lang_name: string
              traditional_chinese: struct<alias: string>
                child 0, alias: string
              no: struct<alias: string>
                child 0, alias: string
              zh: struct<alias: string>
                child 0, alias: string
              url: string
              to
              {'package_version': Value(dtype='string', id=None), 'argos_version': Value(dtype='string', id=None), 'from_code': Value(dtype='string', id=None), 'from_name': Value(dtype='string', id=None), 'to_code': Value(dtype='string', id=None), 'to_name': Value(dtype='string', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2013, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 141 new columns ({'tamil', 'sme', 'uk', 'orv', 'nn', 'lv', 'be', 'thai', 'af', 'gl', 'sk', 'turkish', 'ancient_greek', 'indonesian', 'livvi', 'scottish_gaelic', 'es', 'norwegian_bokmaal', 'lzh', 'id', 'classical_chinese', 'italian', 'spanish', 'coptic', 'bulgarian', 'he', 'ko', 'czech', 'fa', 'fro', 'dutch', 'ga', 'cu', 'north_sami', 'ar', 'cop', 'tr', 'french', 'mr', 'kmr', 'zh-hans', 'buryat', 'kurmanji', 'vi', 'zh', 'wolof', 'gothic', 'eu', 'hy', 'upper_sorbian', 'slovenian', 'hindi', 'nb', 'ca', 'olo', 'old_russian', 'sv', 'telugu', 'fr', 'serbian', 'hebrew', 'lithuanian', 'galician', 'irish', 'ru', 'belarusian', 'simplified_chinese', 'latvian', 'it', 'en', 'et', 'bxr', 'th', 'ug', 'ro', 'el', 'fi', 'te', 'latin', 'danish', 'croatian', 'pl', 'swl', 'catalan', 'cs', 'armenian', 'la', 'marathi', 'uyghur', 'got', 'hsb', 'arabic', 'da', 'greek', 'ta', 'russian', 'wo', 'finnish', 'zh-hant', 'ja', 'estonian', 'lt', 'mt', 'vietnamese', 'sl', 'traditional_chinese', 'norwegian_nynorsk', 'romanian', 'german', 'basque', 'sr', 'swedish', 'ur', 'hi', 'persian', 'old_french', 'maltese', 'kk', 'portuguese', 'no', 'japanese', 'pt', 'english', 'nl', 'hungarian', 'bg', 'old_church_slavonic', 'urdu', 'grc', 'slovak', 'kazakh', 'hu', 'polish', 'afrikaans', 'korean', 'swedish_sign_language', 'gd', 'ukrainian', 'url', 'de', 'hr'}) and 6 missing columns ({'to_name', 'to_code', 'from_code', 'from_name', 'package_version', 'argos_version'}).
              
              This happened while the json dataset builder was generating data using
              
              hf://datasets/GeorgeSpark/translationmodels/en_ja/stanza/resources.json (at revision 601abf1a8a103e0f46a1f3761d22bfa71540909c)
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

package_version
string
argos_version
string
from_code
string
from_name
string
to_code
string
to_name
string
ar
dict
arabic
dict
de
dict
german
dict
en
dict
english
dict
es
dict
spanish
dict
fr
dict
french
dict
nl
dict
dutch
dict
ru
dict
russian
dict
zh-hans
dict
simplified_chinese
dict
af
dict
afrikaans
dict
be
dict
belarusian
dict
bg
dict
bulgarian
dict
bxr
dict
buryat
dict
ca
dict
catalan
dict
cop
dict
coptic
dict
cs
dict
czech
dict
cu
dict
old_church_slavonic
dict
da
dict
danish
dict
el
dict
greek
dict
et
dict
estonian
dict
eu
dict
basque
dict
fa
dict
persian
dict
fi
dict
finnish
dict
fro
dict
old_french
dict
ga
dict
irish
dict
gd
dict
scottish_gaelic
dict
gl
dict
galician
dict
got
dict
gothic
dict
grc
dict
ancient_greek
dict
he
dict
hebrew
dict
hi
dict
hindi
dict
hr
dict
croatian
dict
hsb
dict
upper_sorbian
dict
hu
dict
hungarian
dict
hy
dict
armenian
dict
id
dict
indonesian
dict
it
dict
italian
dict
ja
dict
japanese
dict
kk
dict
kazakh
dict
kmr
dict
kurmanji
dict
ko
dict
korean
dict
la
dict
latin
dict
lt
dict
lithuanian
dict
lv
dict
latvian
dict
lzh
dict
classical_chinese
dict
mr
dict
marathi
dict
mt
dict
maltese
dict
nn
dict
norwegian_nynorsk
dict
nb
dict
norwegian_bokmaal
dict
olo
dict
livvi
dict
orv
dict
old_russian
dict
pl
dict
polish
dict
pt
dict
portuguese
dict
ro
dict
romanian
dict
sk
dict
slovak
dict
sl
dict
slovenian
dict
sme
dict
north_sami
dict
sr
dict
serbian
dict
sv
dict
swedish
dict
swl
dict
swedish_sign_language
dict
ta
dict
tamil
dict
te
dict
telugu
dict
th
dict
thai
dict
tr
dict
turkish
dict
ug
dict
uyghur
dict
uk
dict
ukrainian
dict
ur
dict
urdu
dict
vi
dict
vietnamese
dict
wo
dict
wolof
dict
zh-hant
dict
traditional_chinese
dict
no
dict
zh
dict
url
string
1.1
1.1
en
English
ja
Japanese
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null
null
null
null
null
null
null
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{"backward_charlm":{"ccwiki":{"md5":"a6bd5a290149d920e7fe8c1da4d7e25b"}},"forward_charlm":{"ccwiki":(...TRUNCATED)
{ "alias": "ar" }
{"backward_charlm":{"newswiki":{"md5":"82f0d9a6a12c736648edbe28d87e9016"}},"forward_charlm":{"newswi(...TRUNCATED)
{ "alias": "de" }
{"backward_charlm":{"1billion":{"md5":"1405948b125b4264fd17509d1b6175ca"},"mimic":{"md5":"19a16ba4b8(...TRUNCATED)
{ "alias": "en" }
{"backward_charlm":{"newswiki":{"md5":"acd1d5f0acc5ef8ccbac8576f1676503"}},"forward_charlm":{"newswi(...TRUNCATED)
{ "alias": "es" }
{"backward_charlm":{"newswiki":{"md5":"42eba035046673e195274f45e713653c"}},"forward_charlm":{"newswi(...TRUNCATED)
{ "alias": "fr" }
{"backward_charlm":{"ccwiki":{"md5":"217bcb073b6a5011e684ef8f1ddfb968"}},"forward_charlm":{"ccwiki":(...TRUNCATED)
{ "alias": "nl" }
{"backward_charlm":{"newswiki":{"md5":"47d7517555684206f327ba4e0527d034"}},"forward_charlm":{"newswi(...TRUNCATED)
{ "alias": "ru" }
{"backward_charlm":{"gigaword":{"md5":"9efba48c365d8ad4dfd62587578fa132"}},"forward_charlm":{"gigawo(...TRUNCATED)
{ "alias": "zh-hans" }
{"depparse":{"afribooms":{"md5":"c8d6410c6ec7437e07b70751156b9211","dependencies":[{"model":"pretrai(...TRUNCATED)
{ "alias": "af" }
{"depparse":{"hse":{"md5":"25dfb867053bf31600c9fb547cd9ee2a","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "be" }
{"depparse":{"btb":{"md5":"ddf666ab61c253ff8b2a92945524cd2e","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "bg" }
{"depparse":{"bdt":{"md5":"12301d68999309503f7e5fefd42f55b9","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "bxr" }
{"depparse":{"ancora":{"md5":"3cd02727968bf9fcc41f0ce71f763c56","dependencies":[{"model":"pretrain",(...TRUNCATED)
{ "alias": "ca" }
{"depparse":{"scriptorium":{"md5":"220899cfe651d2a103e02c4516c65040"}},"lemma":{"scriptorium":{"md5"(...TRUNCATED)
{ "alias": "cop" }
{"depparse":{"cac":{"md5":"dcdd27e7680d3a2db2670e5e08b1b079","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "cs" }
{"depparse":{"proiel":{"md5":"a3000d33c8f15a1d1e43715003b96d81","dependencies":[{"model":"pretrain",(...TRUNCATED)
{ "alias": "cu" }
{"depparse":{"ddt":{"md5":"fd3138358cc1855e054498cf11445616","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "da" }
{"depparse":{"gdt":{"md5":"5d8b2a51ed363c441fd9895ce0a54756","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "el" }
{"depparse":{"edt":{"md5":"be9e8f12e52198211e6c4e6eb45c03ce","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "et" }
{"depparse":{"bdt":{"md5":"f69d4045653a7f76fa490a43b9035cf3","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "eu" }
{"depparse":{"seraji":{"md5":"c3a03301089c740262eb0276ad4bfd2a","dependencies":[{"model":"pretrain",(...TRUNCATED)
{ "alias": "fa" }
{"depparse":{"ftb":{"md5":"bb2dcd7d8be332ac38f9a2d842887813","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "fi" }
{"depparse":{"srcmf":{"md5":"5c33458d8bc40eba4985af3f7e2eac27","dependencies":[{"model":"pretrain","(...TRUNCATED)
{ "alias": "fro" }
{"depparse":{"idt":{"md5":"756e74f349bf0176904d96fa65ae05c7","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "ga" }
{"depparse":{"arcosg":{"md5":"0e9b859c8c867bd6f3d4c7986d230e10","dependencies":[{"model":"pretrain",(...TRUNCATED)
{ "alias": "gd" }
{"depparse":{"ctg":{"md5":"2784179b5ea224f451cde93d99babc73","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "gl" }
{"depparse":{"proiel":{"md5":"f9e5760ebe1a8ebe933fa7a2248e92d9","dependencies":[{"model":"pretrain",(...TRUNCATED)
{ "alias": "got" }
{"depparse":{"perseus":{"md5":"d8a055510bb8a0b847d50e348765118a","dependencies":[{"model":"pretrain"(...TRUNCATED)
{ "alias": "grc" }
{"depparse":{"htb":{"md5":"106ada8e20c8856d5e1e7693e99ead05","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "he" }
{"depparse":{"hdtb":{"md5":"50d3dcaef001037ad6ef2dcdf86dfc97","dependencies":[{"model":"pretrain","p(...TRUNCATED)
{ "alias": "hi" }
{"depparse":{"set":{"md5":"e51b4f62d5256288265c7916815e4679","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "hr" }
{"depparse":{"ufal":{"md5":"e66ab0c430b2f1ceaea0b1797c08ef21","dependencies":[{"model":"pretrain","p(...TRUNCATED)
{ "alias": "hsb" }
{"depparse":{"szeged":{"md5":"6789f06dbb6190224f051ceaece20400","dependencies":[{"model":"pretrain",(...TRUNCATED)
{ "alias": "hu" }
{"depparse":{"armtdp":{"md5":"97480899830157d81cfde70077a53336","dependencies":[{"model":"pretrain",(...TRUNCATED)
{ "alias": "hy" }
{"depparse":{"gsd":{"md5":"5d62d3bdbec2df1c38d913eb57f38571","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "id" }
{"depparse":{"isdt":{"md5":"d9299667bc1c3e665d31eb00435af189","dependencies":[{"model":"pretrain","p(...TRUNCATED)
{ "alias": "it" }
{"depparse":{"gsd":{"md5":"d62dd92d5861365aef229cbb827c2555","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "ja" }
{"depparse":{"ktb":{"md5":"3433c67ff4b209542429acc73eee101a","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "kk" }
{"depparse":{"mg":{"md5":"8ebea8cf57387e78fe2bef7a028882f5","dependencies":[{"model":"pretrain","pac(...TRUNCATED)
{ "alias": "kmr" }
{"depparse":{"gsd":{"md5":"26e37dfe8c35c9991d8cb5a3ef95c533","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "ko" }
{"depparse":{"ittb":{"md5":"86709054294657828d8905a888b85933","dependencies":[{"model":"pretrain","p(...TRUNCATED)
{ "alias": "la" }
{"depparse":{"alksnis":{"md5":"c055cd6a85a7480f8bfe89197cc01d47","dependencies":[{"model":"pretrain"(...TRUNCATED)
{ "alias": "lt" }
{"depparse":{"lvtb":{"md5":"fb8fc55e60660415dcdd71efe3f23d8a","dependencies":[{"model":"pretrain","p(...TRUNCATED)
{ "alias": "lv" }
{"depparse":{"kyoto":{"md5":"87d739419339e7f1cf99f108ba65270f","dependencies":[{"model":"pretrain","(...TRUNCATED)
{ "alias": "lzh" }
{"depparse":{"ufal":{"md5":"734df4dda27136859cd11b61ece5044a","dependencies":[{"model":"pretrain","p(...TRUNCATED)
{ "alias": "mr" }
{"depparse":{"mudt":{"md5":"9408df2a1a5aab897508062fef20f39b","dependencies":[{"model":"pretrain","p(...TRUNCATED)
{ "alias": "mt" }
{"depparse":{"nynorsk":{"md5":"48559c93aa1976c0843a8bcf21d2c045","dependencies":[{"model":"pretrain"(...TRUNCATED)
{ "alias": "nn" }
{"depparse":{"bokmaal":{"md5":"8cc47174844d85f27598ac908b5829bd","dependencies":[{"model":"pretrain"(...TRUNCATED)
{ "alias": "nb" }
{"depparse":{"kkpp":{"md5":"d70964ac7c6df6a53138ce44155bde0f","dependencies":[{"model":"pretrain","p(...TRUNCATED)
{ "alias": "olo" }
{"depparse":{"torot":{"md5":"add1c84eaa2f8e009e657de2b5a281a4"}},"lemma":{"torot":{"md5":"6d390806fa(...TRUNCATED)
{ "alias": "orv" }
{"depparse":{"lfg":{"md5":"9bc6038a71540504d6c59711b7af93ea","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "pl" }
{"depparse":{"bosque":{"md5":"314934b3fb237d450124c34378bb3841","dependencies":[{"model":"pretrain",(...TRUNCATED)
{ "alias": "pt" }
{"depparse":{"nonstandard":{"md5":"5d246f49ad580e1ebbc575a024af00ca","dependencies":[{"model":"pretr(...TRUNCATED)
{ "alias": "ro" }
{"depparse":{"snk":{"md5":"9833f9c25092b855485e96a6db163f07","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "sk" }
{"depparse":{"ssj":{"md5":"eb10cbde390cf5f671119a06610effb5","dependencies":[{"model":"pretrain","pa(...TRUNCATED)
{ "alias": "sl" }
{"depparse":{"giella":{"md5":"d719142327ad4e1e6b913acb430416b1","dependencies":[{"model":"pretrain",(...TRUNCATED)
{ "alias": "sme" }
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Dutch
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http://nlp.stanford.edu/software/stanza
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1.1
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Chinese
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http://nlp.stanford.edu/software/stanza
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1.4
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Dutch
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http://nlp.stanford.edu/software/stanza
End of preview.