AntoineBlanot/roberta-span-detection
Token Classification
•
Updated
•
21
tokens
sequence | tags
sequence |
---|---|
[
"can",
"you",
"find",
"me",
"the",
"cheapest",
"mexican",
"restaurant",
"nearby"
] | [
0,
0,
0,
0,
0,
9,
14,
0,
5
] |
[
"can",
"you",
"find",
"me",
"the",
"closed",
"burger",
"king"
] | [
0,
0,
0,
0,
0,
5,
7,
8
] |
[
"can",
"you",
"find",
"me",
"the",
"closest",
"cheesecake",
"factory"
] | [
0,
0,
0,
0,
0,
5,
7,
8
] |
[
"can",
"you",
"find",
"me",
"the",
"closet",
"mc",
"donalds"
] | [
0,
0,
0,
0,
0,
5,
7,
8
] |
[
"can",
"you",
"find",
"me",
"the",
"coast",
"line",
"grill",
"nearby",
"with",
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"at",
"the",
"bar"
] | [
0,
0,
0,
0,
0,
7,
8,
8,
5,
0,
3,
4,
4,
4
] |
[
"can",
"you",
"find",
"me",
"the",
"location",
"of",
"daisy",
"gs",
"it",
"has",
"hotel",
"dining"
] | [
0,
0,
0,
0,
0,
0,
0,
7,
8,
0,
0,
3,
4
] |
[
"can",
"you",
"find",
"me",
"the",
"nearest",
"mcdonalds"
] | [
0,
0,
0,
0,
0,
5,
7
] |
[
"can",
"you",
"find",
"me",
"the",
"nearest",
"pizzeria"
] | [
0,
0,
0,
0,
0,
5,
14
] |
[
"can",
"you",
"find",
"me",
"the",
"nicest",
"restaurant",
"for",
"italian",
"food"
] | [
0,
0,
0,
0,
0,
1,
0,
0,
14,
0
] |
[
"can",
"you",
"find",
"me",
"the",
"phone",
"number",
"for",
"ihop",
"on",
"tilburg",
"street"
] | [
0,
0,
0,
0,
0,
0,
0,
0,
7,
0,
5,
6
] |
[
"can",
"you",
"find",
"me",
"the",
"restaurant",
"with",
"the",
"best",
"reviews"
] | [
0,
0,
0,
0,
0,
0,
0,
0,
1,
2
] |
[
"can",
"you",
"find",
"some",
"reviews",
"on",
"the",
"new",
"restaurant",
"henpeckss"
] | [
0,
0,
0,
0,
1,
0,
0,
0,
0,
7
] |
[
"can",
"you",
"find",
"the",
"bar",
"cadete",
"enterprise",
"near",
"west",
"prescott",
"street"
] | [
0,
0,
0,
0,
3,
7,
8,
5,
6,
6,
6
] |
[
"can",
"you",
"find",
"the",
"black",
"olive",
"within",
"5",
"miles",
"that",
"offers",
"group",
"dining"
] | [
0,
0,
0,
0,
7,
8,
5,
6,
6,
0,
0,
3,
4
] |
[
"can",
"you",
"find",
"the",
"closest",
"taco",
"bell"
] | [
0,
0,
0,
0,
5,
7,
8
] |
[
"can",
"you",
"find",
"the",
"closet",
"ihop"
] | [
0,
0,
0,
0,
5,
7
] |
[
"can",
"you",
"find",
"the",
"locations",
"and",
"phone",
"numbers",
"of",
"all",
"the",
"italian",
"restaurants",
"in",
"the",
"area"
] | [
0,
0,
0,
0,
5,
0,
0,
0,
0,
0,
0,
14,
0,
0,
0,
0
] |
[
"can",
"you",
"find",
"the",
"nearest",
"italian",
"restaurant"
] | [
0,
0,
0,
0,
5,
14,
0
] |
[
"can",
"you",
"find",
"the",
"nearest",
"mexican",
"restaurant"
] | [
0,
0,
0,
0,
5,
14,
0
] |
[
"can",
"you",
"find",
"the",
"nearest",
"pizza",
"place",
"that",
"is",
"still",
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] | [
0,
0,
0,
0,
5,
14,
5,
0,
0,
10,
11
] |
[
"can",
"you",
"find",
"the",
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"the",
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] | [
0,
0,
0,
0,
0,
0,
0,
0,
5,
3,
4,
0
] |
[
"can",
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"italian",
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"here",
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"a",
"family",
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] | [
0,
0,
0,
0,
0,
7,
8,
8,
5,
6,
0,
0,
0,
3,
4
] |
[
"can",
"you",
"find",
"the",
"restaurant",
"lucky",
"fortune",
"with",
"a",
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] | [
0,
0,
0,
0,
0,
7,
8,
0,
0,
1,
2
] |
[
"can",
"you",
"find",
"the",
"restaurant",
"marco",
"polo"
] | [
0,
0,
0,
0,
0,
7,
8
] |
[
"can",
"you",
"find",
"the",
"restaurant",
"passims",
"kitchen",
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"that",
"has",
"late",
"dining"
] | [
0,
0,
0,
0,
0,
7,
8,
5,
6,
0,
0,
10,
11
] |
[
"can",
"you",
"find",
"the",
"restaurant",
"that",
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"closest",
"to",
"me"
] | [
0,
0,
0,
0,
0,
0,
0,
5,
6,
6
] |
[
"can",
"you",
"find",
"us",
"a",
"cheap",
"place",
"to",
"eat"
] | [
0,
0,
0,
0,
0,
9,
0,
0,
0
] |
[
"can",
"you",
"find",
"us",
"a",
"cheap",
"place",
"to",
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"near",
"the",
"park"
] | [
0,
0,
0,
0,
0,
9,
0,
0,
0,
5,
6,
6
] |
[
"can",
"you",
"find",
"us",
"a",
"kid",
"friendly",
"restaurant",
"near",
"by"
] | [
0,
0,
0,
0,
0,
3,
4,
0,
5,
6
] |
[
"can",
"you",
"find",
"us",
"a",
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] | [
0,
0,
0,
0,
0,
14,
0,
5
] |
[
"can",
"you",
"find",
"us",
"a",
"place",
"to",
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] | [
0,
0,
0,
0,
0,
0,
0,
0,
5,
6,
6
] |
[
"can",
"you",
"fine",
"me",
"a",
"moderate",
"priced",
"causual",
"dining",
"restraunt",
"in",
"harvard",
"square"
] | [
0,
0,
0,
0,
0,
9,
0,
3,
4,
0,
0,
5,
6
] |
[
"can",
"you",
"get",
"me",
"a",
"fast",
"food",
"place",
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] | [
0,
0,
0,
0,
0,
14,
16,
5,
0,
0,
9,
15
] |
[
"can",
"you",
"get",
"me",
"a",
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"at",
"an",
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"fine",
"dining",
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] | [
0,
0,
0,
0,
0,
3,
0,
0,
9,
3,
4,
0
] |
[
"can",
"you",
"get",
"me",
"a",
"reservation",
"to",
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"in",
"roslyn",
"ny"
] | [
0,
0,
0,
0,
0,
0,
0,
7,
0,
5,
6
] |
[
"can",
"you",
"get",
"me",
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"to",
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"bees"
] | [
0,
0,
0,
0,
0,
0,
7,
8
] |
[
"can",
"you",
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"me",
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] | [
0,
0,
0,
0,
0,
0,
0,
5,
14,
0
] |
[
"can",
"you",
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"me",
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] | [
0,
0,
0,
0,
0,
0,
0,
5,
7
] |
[
"can",
"you",
"get",
"me",
"the",
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"of",
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"bertuccis"
] | [
0,
0,
0,
0,
0,
0,
0,
0,
5,
7
] |
[
"can",
"you",
"get",
"me",
"the",
"number",
"of",
"the",
"burrito",
"place",
"on",
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"street"
] | [
0,
0,
0,
0,
0,
0,
0,
0,
14,
0,
0,
5,
6
] |
[
"can",
"you",
"get",
"me",
"the",
"number",
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] | [
0,
0,
0,
0,
0,
0,
0,
7
] |
[
"can",
"you",
"get",
"me",
"to",
"a",
"diner",
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"accepts",
"american",
"express"
] | [
0,
0,
0,
0,
0,
0,
14,
0,
3,
4,
4
] |
[
"can",
"you",
"get",
"take",
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"at",
"this",
"restaurant"
] | [
0,
0,
0,
3,
4,
0,
0,
0
] |
[
"can",
"you",
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] | [
0,
0,
0,
0,
0,
0,
0,
0,
14,
3,
0,
5,
6,
6
] |
[
"can",
"you",
"get",
"us",
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"barrel"
] | [
0,
0,
0,
0,
0,
0,
7,
8
] |
[
"can",
"you",
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"area"
] | [
0,
0,
0,
0,
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0,
0,
0,
0,
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4,
0,
5,
6,
6
] |
[
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"you",
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"me",
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"of",
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] | [
0,
0,
0,
0,
0,
0,
0,
5,
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3,
4,
5
] |
[
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"you",
"give",
"me",
"a",
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"of",
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] | [
0,
0,
0,
0,
0,
7,
8,
0,
0,
3,
4,
4
] |
[
"can",
"you",
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"me",
"a",
"list",
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"that",
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] | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
14,
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] |
[
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"me",
"a",
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] | [
0,
0,
0,
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0,
0,
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0,
0,
9,
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6,
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] |
[
"can",
"you",
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"me",
"a",
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"places",
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] | [
0,
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0,
0,
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1,
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0,
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[
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] | [
0,
0,
0,
0,
0,
0,
0,
0,
0,
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] |
[
"can",
"you",
"give",
"me",
"directions",
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] | [
0,
0,
0,
0,
0,
0,
0,
5,
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0
] |
[
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"give",
"me",
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] | [
0,
0,
0,
0,
0,
0,
0,
5,
7,
8,
8
] |
[
"can",
"you",
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"me",
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] | [
0,
0,
0,
0,
0,
0,
0,
7
] |
[
"can",
"you",
"give",
"me",
"some",
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] | [
0,
0,
0,
0,
0,
1,
0,
5,
0,
0,
12
] |
[
"can",
"you",
"give",
"me",
"the",
"address",
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] | [
0,
0,
0,
0,
0,
0,
0,
0,
14,
0
] |
[
"can",
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"give",
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0,
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5,
7,
8,
8,
8,
8,
0,
0,
0,
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0
] |
[
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"to",
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] | [
0,
0,
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0,
0,
0,
0,
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[
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] |
[
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[
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] | [
0,
0,
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0,
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] |
[
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0,
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0,
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3,
4,
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4,
4,
4,
4
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[
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"you",
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"find",
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0,
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[
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16,
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0,
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0,
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10,
11,
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] |
[
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9,
0,
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[
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0,
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[
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] | [
0,
0,
0,
0,
0,
0,
3,
0,
0,
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12
] |
[
"can",
"you",
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"me",
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] | [
0,
0,
0,
0,
0,
0,
1,
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16
] |
[
"can",
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"me",
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] | [
0,
0,
0,
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0,
0,
14,
0,
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0
] |
[
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MIT Restaurant NER dataset formatted in a part of TNER project.
Rating
, Amenity
, Location
, Restaurant_Name
, Price
, Hours
, Dish
, Cuisine
.An example of train
looks as follows.
{
'tags': [0, 0, 0, 0, 0, 0, 0, 0, 5, 3, 4, 0],
'tokens': ['can', 'you', 'find', 'the', 'phone', 'number', 'for', 'the', 'closest', 'family', 'style', 'restaurant']
}
The label2id dictionary can be found at here.
{
"O": 0,
"B-Rating": 1,
"I-Rating": 2,
"B-Amenity": 3,
"I-Amenity": 4,
"B-Location": 5,
"I-Location": 6,
"B-Restaurant_Name": 7,
"I-Restaurant_Name": 8,
"B-Price": 9,
"B-Hours": 10,
"I-Hours": 11,
"B-Dish": 12,
"I-Dish": 13,
"B-Cuisine": 14,
"I-Price": 15,
"I-Cuisine": 16
}
name | train | validation | test |
---|---|---|---|
mit_restaurant | 6900 | 760 | 1521 |