AntoineBlanot/roberta-span-detection
Token Classification
•
Updated
•
48
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",
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] | [
0,
0,
0,
0,
0,
5,
7,
8
] |
[
"can",
"you",
"find",
"me",
"the",
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] | [
0,
0,
0,
0,
0,
5,
7,
8
] |
[
"can",
"you",
"find",
"me",
"the",
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"mc",
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] | [
0,
0,
0,
0,
0,
5,
7,
8
] |
[
"can",
"you",
"find",
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"with",
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0,
0,
0,
0,
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7,
8,
8,
5,
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3,
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4,
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[
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] | [
0,
0,
0,
0,
0,
0,
0,
7,
8,
0,
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3,
4
] |
[
"can",
"you",
"find",
"me",
"the",
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] | [
0,
0,
0,
0,
0,
5,
7
] |
[
"can",
"you",
"find",
"me",
"the",
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] | [
0,
0,
0,
0,
0,
5,
14
] |
[
"can",
"you",
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"me",
"the",
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] | [
0,
0,
0,
0,
0,
1,
0,
0,
14,
0
] |
[
"can",
"you",
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"me",
"the",
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"for",
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] | [
0,
0,
0,
0,
0,
0,
0,
0,
7,
0,
5,
6
] |
[
"can",
"you",
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"me",
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] | [
0,
0,
0,
0,
0,
0,
0,
0,
1,
2
] |
[
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"you",
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"on",
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] | [
0,
0,
0,
0,
1,
0,
0,
0,
0,
7
] |
[
"can",
"you",
"find",
"the",
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"enterprise",
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"west",
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"street"
] | [
0,
0,
0,
0,
3,
7,
8,
5,
6,
6,
6
] |
[
"can",
"you",
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] | [
0,
0,
0,
0,
7,
8,
5,
6,
6,
0,
0,
3,
4
] |
[
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"you",
"find",
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] | [
0,
0,
0,
0,
5,
7,
8
] |
[
"can",
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0,
0,
0,
0,
5,
7
] |
[
"can",
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0,
0,
0,
0,
5,
0,
0,
0,
0,
0,
0,
14,
0,
0,
0,
0
] |
[
"can",
"you",
"find",
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] | [
0,
0,
0,
0,
5,
14,
0
] |
[
"can",
"you",
"find",
"the",
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] | [
0,
0,
0,
0,
5,
14,
0
] |
[
"can",
"you",
"find",
"the",
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] | [
0,
0,
0,
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5,
14,
5,
0,
0,
10,
11
] |
[
"can",
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] | [
0,
0,
0,
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0,
0,
0,
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3,
4,
0
] |
[
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0,
0,
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0,
0,
7,
8,
8,
5,
6,
0,
0,
0,
3,
4
] |
[
"can",
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] | [
0,
0,
0,
0,
0,
7,
8,
0,
0,
1,
2
] |
[
"can",
"you",
"find",
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"marco",
"polo"
] | [
0,
0,
0,
0,
0,
7,
8
] |
[
"can",
"you",
"find",
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] | [
0,
0,
0,
0,
0,
7,
8,
5,
6,
0,
0,
10,
11
] |
[
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"you",
"find",
"the",
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"me"
] | [
0,
0,
0,
0,
0,
0,
0,
5,
6,
6
] |
[
"can",
"you",
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"us",
"a",
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] | [
0,
0,
0,
0,
0,
9,
0,
0,
0
] |
[
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"us",
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] | [
0,
0,
0,
0,
0,
9,
0,
0,
0,
5,
6,
6
] |
[
"can",
"you",
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"us",
"a",
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"by"
] | [
0,
0,
0,
0,
0,
3,
4,
0,
5,
6
] |
[
"can",
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"us",
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0,
0,
0,
0,
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14,
0,
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[
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0,
0,
0,
0,
0,
0,
0,
0,
5,
6,
6
] |
[
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"you",
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] | [
0,
0,
0,
0,
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3,
4,
0,
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] |
[
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0,
0,
0,
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16,
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0,
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] |
[
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] | [
0,
0,
0,
0,
0,
3,
0,
0,
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3,
4,
0
] |
[
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"you",
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"me",
"a",
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] | [
0,
0,
0,
0,
0,
0,
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7,
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5,
6
] |
[
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"me",
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] | [
0,
0,
0,
0,
0,
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] |
[
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0,
0,
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0,
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5,
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[
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0,
0,
0,
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] |
[
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0,
0,
0,
0,
0,
0,
0,
0,
5,
7
] |
[
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"me",
"the",
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0,
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0,
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[
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] | [
0,
0,
0,
0,
0,
0,
0,
7
] |
[
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"me",
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] | [
0,
0,
0,
0,
0,
0,
14,
0,
3,
4,
4
] |
[
"can",
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"get",
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] | [
0,
0,
0,
3,
4,
0,
0,
0
] |
[
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] |
[
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] | [
0,
0,
0,
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0,
0,
7,
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] |
[
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[
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[
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] |
[
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[
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[
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[
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[
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[
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0,
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] |
[
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0,
0,
0,
0,
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0,
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7
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[
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] | [
0,
0,
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0,
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0,
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] |
[
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0,
0,
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
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[
"can",
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0,
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[
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[
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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 |