Sentence #
stringlengths
11
15
Word
stringlengths
1
64
POS
stringclasses
42 values
Tag
stringclasses
17 values
Sentence: 1
Thousands
NNS
O
null
of
IN
O
null
demonstrators
NNS
O
null
have
VBP
O
null
marched
VBN
O
null
through
IN
O
null
London
NNP
B-geo
null
to
TO
O
null
protest
VB
O
null
the
DT
O
null
war
NN
O
null
in
IN
O
null
Iraq
NNP
B-geo
null
and
CC
O
null
demand
VB
O
null
the
DT
O
null
withdrawal
NN
O
null
of
IN
O
null
British
JJ
B-gpe
null
troops
NNS
O
null
from
IN
O
null
that
DT
O
null
country
NN
O
null
.
.
O
Sentence: 2
Families
NNS
O
null
of
IN
O
null
soldiers
NNS
O
null
killed
VBN
O
null
in
IN
O
null
the
DT
O
null
conflict
NN
O
null
joined
VBD
O
null
the
DT
O
null
protesters
NNS
O
null
who
WP
O
null
carried
VBD
O
null
banners
NNS
O
null
with
IN
O
null
such
JJ
O
null
slogans
NNS
O
null
as
IN
O
null
null
``
O
null
Bush
NNP
B-per
null
Number
NN
O
null
One
CD
O
null
Terrorist
NN
O
null
null
``
O
null
and
CC
O
null
null
``
O
null
Stop
VB
O
null
the
DT
O
null
Bombings
NNS
O
null
.
.
O
null
null
``
O
Sentence: 3
They
PRP
O
null
marched
VBD
O
null
from
IN
O
null
the
DT
O
null
Houses
NNS
O
null
of
IN
O
null
Parliament
NN
O
null
to
TO
O
null
a
DT
O
null
rally
NN
O
null
in
IN
O
null
Hyde
NNP
B-geo
null
Park
NNP
I-geo
null
.
.
O
Sentence: 4
Police
NNS
O
null
put
VBD
O
null
the
DT
O
null
number
NN
O
null
of
IN
O
null
marchers
NNS
O
null
at
IN
O
null
10,000
CD
O
null
while
IN
O
null
organizers
NNS
O
null
claimed
VBD
O
null
it
PRP
O
null
was
VBD
O
null
1,00,000
CD
O
null
.
.
O
Sentence: 5
The
DT
O
null
protest
NN
O
null
comes
VBZ
O
null
on
IN
O
null
the
DT
O
null
eve
NN
O
null
of
IN
O
null
the
DT
O
null
annual
JJ
O
null
conference
NN
O
null
of
IN
O
null
Britain
NNP
B-geo
null
's
POS
O
null
ruling
VBG
O
null
Labor
NNP
B-org
null
Party
NNP
I-org
null
in
IN
O
YAML Metadata Warning: The task_categories "Name Entity Recognisition" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, any-to-any, other

Dataset : Name Entity Recognition

The dataset used is a custom NER dataset provided in CSV format with columns:

sentence_id: Unique identifier for sentences. words: The words in each sentence. labels: The named entity labels corresponding to each word.

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Models trained or fine-tuned on SriramRokkam/ner_dataset.csv