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Datasets:
stefan-it
/
germeval14_no_wikipedia

Tasks:
Token Classification
Languages:
German
License:
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The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code:   DatasetGenerationError
Exception:    ParserError
Message:      Error tokenizing data. C error: EOF inside string starting at row 117511
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1995, in _prepare_split_single
                  for _, table in generator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/csv/csv.py", line 195, in _generate_tables
                  for batch_idx, df in enumerate(csv_file_reader):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
                  return self.get_chunk()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
                  return self.read(nrows=size)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1923, in read
                  ) = self._engine.read(  # type: ignore[attr-defined]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
                  chunks = self._reader.read_low_memory(nrows)
                File "parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
                File "parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
                File "parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
              pandas.errors.ParserError: Error tokenizing data. C error: EOF inside string starting at row 117511
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1529, 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 1154, 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 2038, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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#
string
n-tv.de vom 26.02.2005
string
[2005-02-26]
string
__index_level_0__
string
Schartau
B-PER
O
1
sagte
O
O
2
dem
O
O
3
O O 5 Tagesspiegel B-ORG O 6
O
O
4
vom
O
O
7
Freitag
O
O
8
,
O
O
9
Fischer
B-PER
O
10
sei
O
O
11
O O 13 in O O 14 einer O O 15 Weise O O 16 aufgetreten O O 17 , O O 18 die O O 19 alles O O 20 andere O O 21 als O O 22 überzeugend O O 23 war O O 24
O
O
12
.
O
O
25
welt.de vom 29.10.2005
[2005-10-29]
null
#
Firmengründer
O
O
1
Wolf
B-PER
O
2
Peter
I-PER
O
3
Bree
I-PER
O
4
arbeitete
O
O
5
Anfang
O
O
6
der
O
O
7
siebziger
O
O
8
Jahre
O
O
9
als
O
O
10
Möbelvertreter
O
O
11
,
O
O
12
als
O
O
13
er
O
O
14
einen
O
O
15
fliegenden
O
O
16
Händler
O
O
17
aus
O
O
18
dem
O
O
19
Libanon
B-LOC
O
20
traf
O
O
21
.
O
O
22
http://www.stern.de/sport/fussball/krawalle-in-der-fussball-bundesliga-dfb-setzt-auf-falsche-konzepte-1553657.html#utm_source=standard&utm_medium=rss-feed&utm_campaign=sport
[2010-03-25]
null
#
Ob
O
O
1
sie
O
O
2
dabei
O
O
3
nach
O
O
4
dem
O
O
5
Runden
O
O
6
Tisch
O
O
7
am
O
O
8
23.
O
O
9
April
O
O
10
in
O
O
11
Berlin
B-LOC
O
12
durch
O
O
13
ein
O
O
14
pädagogisches
O
O
15
Konzept
O
O
16
unterstützt
O
O
17
wird
O
O
18
,
O
O
19
ist
O
O
20
allerdings
O
O
21
zu
O
O
22
bezweifeln
O
O
23
.
O
O
24
stern.de vom 21.03.2006
[2006-03-21]
null
#
Bayern
B-ORG
B-LOC
1
München
I-ORG
B-LOC
2
ist
O
O
3
wieder
O
O
4
alleiniger
O
O
5
Top-
O
O
6
Favorit
O
O
7
auf
O
O
8
den
O
O
9
Gewinn
O
O
10
der
O
O
11
deutschen
B-LOCderiv
O
12
Fußball-Meisterschaft
O
O
13
.
O
O
14
http://www.fr-online.de/in_und_ausland/sport/aktuell/1618625_Frings-schaut-finster-in-die-Zukunft.html
[2008-10-24]
null
#
Dabei
O
O
1
hätte
O
O
2
der
O
O
3
tapfere
O
O
4
Schlussmann
O
O
5
allen
O
O
6
Grund
O
O
7
gehabt
O
O
8
,
O
O
9
sich
O
O
10
viel
O
O
11
früher
O
O
12
aufzuregen
O
O
13
.
O
O
14
handelsblatt.com vom 12.07.2005
[2005-07-12]
null
#
ARD-Programmchef
B-ORGpart
O
1
Günter
B-PER
O
2
Struve
I-PER
O
3
war
O
O
4
wegen
O
O
5
eines
O
O
6
vierwöchigen
O
O
7
Urlaubs
O
O
8
für
O
O
9
eine
O
O
10
End of preview.

Filtered GermEval 2014 NER Dataset

This repository hosts a filtered version of the great GermEval 2014 NER Dataset.

After some analysis of the annotated examples in this dataset, it can be seen that the dataset is highly biased by Wikipedia articles.

Dataset Stats

We present an overview of the top 10 top-level domains where annotations were retrieved from for training, development and test splits:

Training Split

TLD Number of examples (Percentage)
wikipedia.org 12,007 (50.03%)
welt.de 662 (2.76%)
spiegel.de 512 (2.13%)
tagesspiegel.de 424 (1.77%)
handelsblatt.com 369 (1.54%)
fr-aktuell.de 344 (1.43%)
sueddeutsche.de 308 (1.28%)
abendblatt.de 283 (1.18%)
berlinonline.de 255 (1.06%)
szon.de 249 (1.04%)

Development Split

TLD Number of examples (Percentage)
wikipedia.org 1,119 (50.86%)
welt.de 46 (2.09%)
spiegel.de 43 (1.95%)
fr-aktuell.de 38 (1.73%)
tagesspiegel.de 37 (1.68%)
handelsblatt.com 35 (1.59%)
sueddeutsche.de 28 (1.27%)
szon.de 25 (1.14%)
feedsportal.com 24 (1.09%)
berlinonline.de 22 (1.0%)

Test Split

TLD Number of examples (Percentage)
wikipedia.org 2,547 (49.94%)
welt.de 139 (2.73%)
spiegel.de 88 (1.73%)
tagesspiegel.de 86 (1.69%)
handelsblatt.com 84 (1.65%)
sueddeutsche.de 78 (1.53%)
abendblatt.de 72 (1.41%)
fr-aktuell.de 62 (1.22%)
berlinonline.de 59 (1.16%)
szon.de 57 (1.12%)

Summary

For each dataset split it can be seen, that the portion of annotated examples from Wikipedia are around 50%!

Filtered Version & Motivation

We now create a Wikipedia-filtered-out version of the GermEval 2014 dataset. Here's one scenario for the main motivation:

Imagine you are pretraining a nice language model and you want to measure performance on GermEval 2014 for named entity recognition. Additionally, you want of course to compare performance to other existing language models.

What would be the easiest way to get high performance on GermEval 2014 dataset? Yes, you can literally pretrain a language model on Wikipedia only (just as I did)! It will outperform models that are even pretrained on 100+ GB! See the great ScandEval leaderboard and have a look at the gwlms models. However, the model performance for this pretrained model on Wikipedia-only will be worse on other downstream tasks such as Question Answering.

So this Wikipedia-filtered-out version could help to achieve better comparisons between LMs.

Stats for Filtered Version

Additionally, we now present the stats for the filtered version of GermEval 2014 dataset:

Training Split

TLD Number of examples (Percentage)
welt.de 662 (5.52%)
spiegel.de 512 (4.27%)
tagesspiegel.de 424 (3.54%)
handelsblatt.com 369 (3.08%)
fr-aktuell.de 344 (2.87%)
sueddeutsche.de 308 (2.57%)
abendblatt.de 283 (2.36%)
berlinonline.de 255 (2.13%)
szon.de 249 (2.08%)
n-tv.de 195 (1.63%)

Development Split

TLD Number of examples (Percentage)
welt.de 46 (4.26%)
spiegel.de 43 (3.98%)
fr-aktuell.de 38 (3.52%)
tagesspiegel.de 37 (3.42%)
handelsblatt.com 35 (3.24%)
sueddeutsche.de 28 (2.59%)
szon.de 25 (2.31%)
feedsportal.com 24 (2.22%)
berlinonline.de 22 (2.04%)
rp-online.de 21 (1.94%)

Test Split

TLD Number of examples (Percentage)
welt.de 139 (5.44%)
spiegel.de 88 (3.45%)
tagesspiegel.de 86 (3.37%)
handelsblatt.com 84 (3.29%)
sueddeutsche.de 78 (3.06%)
abendblatt.de 72 (2.82%)
fr-aktuell.de 62 (2.43%)
berlinonline.de 59 (2.31%)
szon.de 57 (2.23%)
feedsportal.com 52 (2.04%)

Dataset Creation

We provide a notebook that shows how to recreate this filtered version of GermEval 2014. It can be found here.

Additionally, we provide a dataset loader for the awesome Flair library!

Licence

We keep the original license of GermEval 2014 dataset ( CC-BY-4.0).

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