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MadvaAparna
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KannadaVibhaktiSamples

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Token Classification
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Kannada
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tokens
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12
ner_tags
sequencelengths
5
12
langs
sequencelengths
5
12
spans
sequencelengths
1
2
[ "ರಾಮ್", "ಚರಣ್", "ತೇಜನು", "ತೆಲುಗು", "ಚಿತ್ರರಂಗಕ್ಕೆ", "ಚಿರಪರಿಚಿತ", ".", "" ]
[ 1, 2, 2, 0, 0, 0, 0, 0 ]
[ "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn" ]
[ "PER:ರಾಮ್ ಚರಣ್ ತೇಜನು" ]
[ "ಸಂದರ್ಶನದ", "ದಿನ", "ಮಧ್ಯಾಹ್ನ", "ಲಘು", "ಉಪಹಾರದ", "ವ್ಯವಸ್ಥೆಯನ್ನು", "ಮಾತ್ರ", "ರಂಗಾಯಣದಿಂದ", "ಮಾಡಲಾಗುವುದು", "." ]
[ 0, 0, 0, 0, 0, 0, 0, 3, 0, 0 ]
[ "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn" ]
[ "ORG:ರಂಗಾಯಣದಿಂದ" ]
[ "ಮೈಸೂರಿನಲ್ಲಿ", "ರಂಗಾಯಣವನ್ನು", "ಅಂದಿನ", "ಸರ್ಕಾರ", "ಅಧಿಕ್ರತವಾಗಿ", "ಸ್ಥಾಪಿಸಿತು", ".", "" ]
[ 5, 3, 0, 0, 0, 0, 0, 0 ]
[ "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn" ]
[ "LOC:ಮೈಸೂರಿನಲ್ಲಿ", "ORG:ರಂಗಾಯಣವನ್ನು" ]
[ "ಮುಂದಿನ", "ಯಶಸ್ವೀ", "ನಟ", "ರಾಮ್", "ಚರಣ್", "ತೇಜನೇ", "ಅಂತ", "ಅನಿಸೋದಂತೂ", "ನಿಜ", ".", "" ]
[ 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0 ]
[ "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn" ]
[ "PER:ರಾಮ್ ಚರಣ್ ತೇಜನೇ" ]
[ "ನಿರ್ದೇಶಕರಿಗೆ", "ರಾಮ್", "ಚರಣ್", "ತೇಜನಲ್ಲಿ", "ಅದೇನು", "ನಂಬಿಕೆ", "ಇತ್ತೋ", "ಏನೋ", "!" ]
[ 0, 1, 2, 2, 0, 0, 0, 0, 0 ]
[ "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn" ]
[ "PER:ರಾಮ್ ಚರಣ್ ತೇಜನಲ್ಲಿ" ]
[ "ರಾಮ್", "ಚರಣ್", "ತೇಜನ", "ದೆಸೆಯಿಂದ", "ತೆಲುಗು", "ಚಲನಚಿತ್ರ", "ಜನಪ್ರಿಯ", "ಆಯಿತು", ".", "" ]
[ 1, 2, 2, 0, 0, 0, 0, 0, 0, 0 ]
[ "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn" ]
[ "PER:ರಾಮ್ ಚರಣ್ ತೇಜನ" ]
[ "ರಾಮ್", "ಚರಣ್", "ತೇಜನ", "ಪತ್ನಿ", "ಕೂಡ", "ಒಂದು", "ಯಶಸ್ವೀ", "ಉದ್ಯಮದ", "ಮಾಲಕಿ", ".", "" ]
[ 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0 ]
[ "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn" ]
[ "PER:ರಾಮ್ ಚರಣ್ ತೇಜನ" ]
[ "ರಂಗಾಯಣವೇ", "ವಿದ್ಯಾರ್ಥಿಗಳಿಗೆ", "ವಸತಿ", "ಸೌಕರ್ಯವನ್ನು", "ನೀಡುತ್ತದೆ", ".", "" ]
[ 3, 0, 0, 0, 0, 0, 0 ]
[ "kn", "kn", "kn", "kn", "kn", "kn", "kn" ]
[ "ORG:ರಂಗಾಯಣವೇ" ]
[ "ರಂಗಾಯಣದಲ್ಲಿ", "ಎಲ್ಲರೂ", "ಕನ್ನಡದಲ್ಲಿ", "ವ್ಯವಹರಿಸುವುದು", "ಅತ್ಯಗತ್ಯ", ".", "" ]
[ 3, 0, 0, 0, 0, 0, 0 ]
[ "kn", "kn", "kn", "kn", "kn", "kn", "kn" ]
[ "ORG:ರಂಗಾಯಣದಲ್ಲಿ" ]
[ "RRR", "ಚಿತ್ರಕ್ಕೆ", "ರಾಮ್", "ಚರಣ್", "ತೇಜನನ್ನು", "ಆಯ್ಕೆ", "ಬಹಳ", "ಮುಂಚೆಯೇ", "ಮಾಡಿದ್ದರು", ".", "" ]
[ 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0 ]
[ "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn" ]
[ "PER:ರಾಮ್ ಚರಣ್ ತೇಜನನ್ನು" ]
[ "ರಂಗಾಯಣದ", "ದೆಸೆಯಿಂದ", "ಅತ್ಯುನ್ನತ", "ಕಲಾವಿದರು", "ತರಬೇತಿ", "ಪಡೆಯುವಂತಾಯಿತು", ".", "" ]
[ 3, 0, 0, 0, 0, 0, 0, 0 ]
[ "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn" ]
[ "ORG:ರಂಗಾಯಣದ" ]
[ "ರಾಮ್", "ಚರಣ್", "ತೇಜನಿಗೆ", "ತನ್ನ", "ಕುಟುಂಬದ", "ಜೊತೆ", "ಪ್ರಶಸ್ತಿ", "ಸ್ವೀಕರಿಸಲು", "ಆಹ್ವಾನ", "ಬಂತು", ".", "" ]
[ 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
[ "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn" ]
[ "PER:ರಾಮ್ ಚರಣ್ ತೇಜನಿಗೆ" ]
[ "ರಂಗಾಯಣಕ್ಕೆ", "ಸೇರಬಯಸುವ", "ಅಭ್ಯರ್ಥಿಗೆ", "ಕನಿಷ್ಠ", "ವಿದ್ಯಾರ್ಹತೆ", "ಪಿಯುಸಿ", "ಅಥವಾ", "ತತ್ಸಮಾನ", "ಪಾಸಾಗಿರಬೇಕು", "." ]
[ 3, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
[ "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn" ]
[ "ORG:ರಂಗಾಯಣಕ್ಕೆ" ]
[ "ರಂಗಾಯಣವು", "ಒಂದು", "ಪೂರ್ಣ", "ಪ್ರಮಾಣದ", "ರಂಗ", "ಶಿಕ್ಷಣ", "ಕೇಂದ್ರ", ".", "" ]
[ 3, 0, 0, 0, 0, 0, 0, 0, 0 ]
[ "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn" ]
[ "ORG:ರಂಗಾಯಣವು" ]
[ "ಆತನ", "ಮೊದಲ", "ಚಿತ್ರದಲ್ಲಿ", "ರಾಮ್", "ಚರಣ್", "ತೇಜನಿಂದ", "ಬಹಳ", "ನಿರೀಕ್ಷೆ", "ಇತ್ತು", ".", "" ]
[ 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0 ]
[ "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn", "kn" ]
[ "PER:ರಾಮ್ ಚರಣ್ ತೇಜನಿಂದ" ]
[ "ಸಂದರ್ಶನವನ್ನು", "ರಂಗಾಯಣದ", "ಆವರಣದಲ್ಲಿ", "ನಡೆಸಲಾಗುವುದು", "." ]
[ 0, 3, 0, 0, 0 ]
[ "kn", "kn", "kn", "kn", "kn" ]
[ "ORG:ರಂಗಾಯಣದ" ]

Dataset Card for Dataset Name

This is a dataset for the Named Entity Recognition (NER) task in Kannada language. Each instance represents one of the vibhakti cases.

Dataset Details

Dataset Description

Kannada language supports eight (grammatical) cases [Refer https://kannadakalike.org/grammar/cases]. The cases are called vibhakti and corresponding suffixes, pratyaya. Noun words are inflected with the suffix corresponding to these cases to create another grammatically meaningful word.

In this dataset, 12 sentences are composed for each case, thus resulting in a total of 96 sentences. Each sentence contains at least one word representing any named entity which is inflected with the suffix for the corresponding case. We include examples for PER, LOC and ORG class of entities. For PER class, we also include entities which span across multiple words such as caMdragupta maurya. This vibhakti dataset can be used to analyse how the presence of case specific suffixes in a named entity affects the prediction by the model.

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Uses

Dataset can be used in case of the NER task.

Dataset Structure

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Source Data

Manually created

Who are the annotators?

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