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milistu
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GUM-NER-conll

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Token Classification
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- ---
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- dataset_info:
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- features:
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- - name: id
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- dtype: string
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- - name: words
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- sequence: string
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- - name: ner_tags
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- sequence:
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- class_label:
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- names:
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- '0': O
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- '1': B-abstract
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- '2': I-abstract
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- '3': B-animal
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- '4': I-animal
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- '5': B-event
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- '6': I-event
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- '7': B-object
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- '8': I-object
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- '9': B-organization
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- '10': I-organization
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- '11': B-person
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- '12': I-person
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- '13': B-place
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- '14': I-place
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- '15': B-plant
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- '16': I-plant
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- '17': B-quantity
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- '18': I-quantity
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- '19': B-substance
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- '20': I-substance
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- '21': B-time
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- '22': I-time
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- splits:
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- - name: train
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- num_bytes: 754935
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- num_examples: 2495
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- - name: test
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- num_bytes: 311483
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- num_examples: 1000
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- download_size: 296638
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- dataset_size: 1066418
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- configs:
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- - config_name: default
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- data_files:
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- - split: train
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- path: data/train-*
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- - split: test
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- path: data/test-*
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- task_categories:
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- - token-classification
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- language:
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- - en
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- size_categories:
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- - 1K<n<10K
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- ---
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  # GUM: The Georgetown University Multilayer Corpus
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  The GUM corpus was collected and annotated at Georgetown University. For more information, see the [LICENSE](https://corpling.uis.georgetown.edu/gum).
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- ## Labels
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  - Number of labels: **23**
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  ```Python
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  ['O',
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- 'B-abstract',
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- 'I-abstract',
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- 'B-animal',
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- 'I-animal',
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- 'B-event',
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- 'I-event',
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- 'B-object',
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- 'I-object',
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- 'B-organization',
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- 'I-organization',
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- 'B-person',
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- 'I-person',
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- 'B-place',
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- 'I-place',
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- 'B-plant',
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- 'I-plant',
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- 'B-quantity',
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- 'I-quantity',
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- 'B-substance',
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- 'I-substance',
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- 'B-time',
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- 'I-time']
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  ```
 
 
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- ### Citation
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
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  @Article{Zeldes2017,
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  author = {Amir Zeldes},
 
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+ ---
2
+ dataset_info:
3
+ features:
4
+ - name: id
5
+ dtype: string
6
+ - name: words
7
+ sequence: string
8
+ - name: ner_tags
9
+ sequence:
10
+ class_label:
11
+ names:
12
+ '0': O
13
+ '1': B-abstract
14
+ '2': I-abstract
15
+ '3': B-animal
16
+ '4': I-animal
17
+ '5': B-event
18
+ '6': I-event
19
+ '7': B-object
20
+ '8': I-object
21
+ '9': B-organization
22
+ '10': I-organization
23
+ '11': B-person
24
+ '12': I-person
25
+ '13': B-place
26
+ '14': I-place
27
+ '15': B-plant
28
+ '16': I-plant
29
+ '17': B-quantity
30
+ '18': I-quantity
31
+ '19': B-substance
32
+ '20': I-substance
33
+ '21': B-time
34
+ '22': I-time
35
+ splits:
36
+ - name: train
37
+ num_bytes: 754935
38
+ num_examples: 2495
39
+ - name: test
40
+ num_bytes: 311483
41
+ num_examples: 1000
42
+ download_size: 296638
43
+ dataset_size: 1066418
44
+ configs:
45
+ - config_name: default
46
+ data_files:
47
+ - split: train
48
+ path: data/train-*
49
+ - split: test
50
+ path: data/test-*
51
+ task_categories:
52
+ - token-classification
53
+ language:
54
+ - en
55
+ size_categories:
56
+ - 1K<n<10K
57
+ ---
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  # GUM: The Georgetown University Multilayer Corpus
59
  The GUM corpus was collected and annotated at Georgetown University. For more information, see the [LICENSE](https://corpling.uis.georgetown.edu/gum).
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+ ## Structure
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  - Number of labels: **23**
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  ```Python
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  ['O',
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+ 'B-abstract', 'I-abstract',
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+ 'B-animal', 'I-animal',
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+ 'B-event', 'I-event',
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+ 'B-object', 'I-object',
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+ 'B-organization', 'I-organization',
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+ 'B-person', 'I-person',
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+ 'B-place', 'I-place',
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+ 'B-plant', 'I-plant',
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+ 'B-quantity', 'I-quantity',
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+ 'B-substance', 'I-substance',
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+ 'B-time', 'I-time']
 
 
 
 
 
 
 
 
 
 
 
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  ```
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+ ### Train set
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+ Number of sentences in the train set: **2495**
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+ Label count in train set:
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+ | Label | Count |
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+ |-----------------|--------|
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+ | O | 20460 |
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+ | I-abstract | 4687 |
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+ | I-event | 2707 |
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+ | I-place | 2212 |
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+ | B-abstract | 2002 |
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+ | B-person | 1920 |
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+ | I-person | 1866 |
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+ | I-object | 1732 |
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+ | B-place | 1150 |
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+ | B-object | 1017 |
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+ | B-event | 738 |
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+ | I-time | 663 |
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+ | I-organization | 552 |
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+ | I-substance | 458 |
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+ | B-time | 401 |
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+ | B-organization | 397 |
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+ | B-substance | 278 |
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+ | I-quantity | 203 |
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+ | I-plant | 166 |
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+ | B-plant | 144 |
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+ | B-animal | 141 |
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+ | I-animal | 120 |
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+ | B-quantity | 97 |
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+
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+
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+ ### Test set
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+ Number of sentences in the test set: **1000**
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+
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+ Label count in test set:
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+ | Label | Count |
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+ |-----------------|-------|
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+ | O | 8543 |
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+ | I-abstract | 2048 |
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+ | I-event | 934 |
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+ | I-place | 926 |
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+ | B-person | 823 |
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+ | B-abstract | 798 |
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+ | I-object | 782 |
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+ | I-person | 685 |
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+ | B-place | 469 |
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+ | B-object | 420 |
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+ | B-event | 315 |
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+ | I-organization | 278 |
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+ | I-time | 242 |
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+ | B-organization | 192 |
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+ | I-substance | 183 |
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+ | B-time | 179 |
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+ | B-substance | 95 |
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+ | I-quantity | 77 |
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+ | B-plant | 62 |
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+ | I-plant | 56 |
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+ | B-quantity | 44 |
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+ | I-animal | 43 |
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+ | B-animal | 42 |
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+
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+
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+ ## Citation
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  ```
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  @Article{Zeldes2017,
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  author = {Amir Zeldes},