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Runs
5
Name
2 visualized
grateful-bird-47
grateful-bird-47
woven-microwave-42
woven-microwave-42
cerulean-salad-27
cerulean-salad-27
cool-tree-26
cool-tree-26
olive-butterfly-23
olive-butterfly-23
1-5
of 5
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Tables
1
runs
.
summary
["
Global metrics
"]
⏎
Filter
0.8299
0.8085
0.8173
0.6722
0.6418
0.7057
0.8799
0.862
0.8799
0.7291
0.7021
0.7581
accuracy_IC
f1_macro_IC
f1_weighted_IC
f1_macro_NER
precision_NER
recall_NER
1
2
Media
2
1-2 of 2
Intent classification f1-score per language
grateful-bird-47
en-US
id-ID
pt-PT
fr-FR
tl-PH
de-DE
ro-RO
zh-CN
cy-GB
fa-IR
ru-RU
zh-TW
sl-SL
ja-JP
te-IN
ta-IN
ar-SA
0
0.2
0.4
0.6
0.8
variable
af-ZA
am-ET
ar-SA
az-AZ
bn-BD
cy-GB
da-DK
de-DE
el-GR
en-US
es-ES
fa-IR
fi-FI
fr-FR
he-IL
hi-IN
hu-HU
hy-AM
id-ID
is-IS
it-IT
ja-JP
jv-ID
ka-GE
km-KH
kn-IN
ko-KR
lv-LV
ml-IN
mn-MN
ms-MY
my-MM
nb-NO
nl-NL
pl-PL
pt-PT
ro-RO
ru-RU
sl-SL
sq-AL
sv-SE
sw-KE
ta-IN
te-IN
th-TH
tl-PH
tr-TR
ur-PK
vi-VN
zh-CN
zh-TW
Intent classification weighted f1-score per language
variable
value
plotly-logomark
cool-tree-26
en-US
nb-NO
af-ZA
es-ES
tl-PH
az-AZ
sq-AL
fa-IR
lv-LV
hu-HU
mn-MN
cy-GB
ru-RU
te-IN
my-MM
he-IL
ka-GE
0
0.2
0.4
0.6
0.8
variable
af-ZA
am-ET
ar-SA
az-AZ
bn-BD
cy-GB
da-DK
de-DE
el-GR
en-US
es-ES
fa-IR
fi-FI
fr-FR
he-IL
hi-IN
hu-HU
hy-AM
id-ID
is-IS
it-IT
ja-JP
jv-ID
ka-GE
km-KH
kn-IN
ko-KR
lv-LV
ml-IN
mn-MN
ms-MY
my-MM
nb-NO
nl-NL
pl-PL
pt-PT
ro-RO
ru-RU
sl-SL
sq-AL
sv-SE
sw-KE
ta-IN
te-IN
th-TH
tl-PH
tr-TR
ur-PK
vi-VN
zh-CN
zh-TW
Intent classification weighted f1-score per language
variable
value
plotly-logomark
Step
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List<File<(table)>>
Ops
.contents
.count
.digest
.dropna
.filter((row) => row)
.isNone
.join(, (row) => row, (row) => row, "", "", , )
.joinToStr("")
.map((row, index) => row)
.merge("")
.size
.table
.table("")
[]
.project
.run