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int64
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complaining
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failing to support
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desperately needs
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many years of decay
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no quick fix
0
is happy
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long and painful
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nobody is happy
0
a very complicated process
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a decade of dramatic economic decline
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suffering from
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would surely be higher still
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had forbidden
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suffering from some intoxication
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far from perfect
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soured relations between
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fell out
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mounting rivalry
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a hostile nation
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have each accused the other
0
bowed to
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totally dependent
0
lose control
0
warned
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bowed to pressure
0
to its knees
0
military adventurism
0
would boost
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threaten
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most heavily armed place in the world
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dangerous flashpoint
0
a superficial exercise
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accusing
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merely speaks
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deep divergences
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negatively
0
biased
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claims
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was biased against
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complaints of
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suspicions
0
squeezing
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playing with fire
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subverts
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trying to provoke protests
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his mdc lot
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will not
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most dangerous
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shoot the man out of office
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that extreme
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keen to fight
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are agitating
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allegedly threatening
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will remove you violently
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villains
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do n't want
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challenged
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flirting with
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at it again
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opposed
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alleged
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wants to speak the language of violence
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shoot president mugabe out of office
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fears
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has refused to bow
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parliamentarian
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still - tense relationship
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our renegade province
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unusual
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so worried
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disapproved
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so - called stop - over tactic
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will not resort
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private diplomacy
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protests
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opposes
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gulf between
0
do not possess the necessary expertise
0
is an act of objectionable brutality
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just to gain popularity
0
may feel uneasy
0
this is a question of
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certain excesses
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does not function optimally
0
is colored by leftist ideology
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hype
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fostering trade anarchy
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this is no blind violence but rather targeted violence
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there is no alternative to it but conflict , isolation , nationalism , and ultimately war
0
cold monster
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immense gulf between
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slow
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are needed
0
the brutality with which this closure was implemented was unheard of
0
thought
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was annoyed
0
lost the game
0
but there is little i can do
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would totally liberalize
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only got worse
0
End of preview. Expand in Data Studio

Dataset used in the paper:

A thorough benchmark of automatic text classification From traditional approaches to large language models

https://github.com/waashk/atcBench

To guarantee the reproducibility of the obtained results, the dataset and its respective CV train-test partitions is available here.

Each dataset contains the following files:

  • data.parquet: pandas DataFrame with texts and associated encoded labels for each document.
  • split_<k>.pkl: pandas DataFrame with k-cross validation partition.
  • For each fold <k>:
    • train_fold_<k>.parquet: pandas DataFrame with texts and associated encoded labels for each document on the training split k (according to split_<k>.pkl)
    • train_fold_<k>.parquet: pandas DataFrame with texts and associated encoded labels for each document on the testing split k (according to split_<k>.pkl)
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