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asahi417 commited on
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a493627
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1 Parent(s): 373bd8e

fix readme

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Files changed (34) hide show
  1. .gitattributes +16 -0
  2. README.md +13 -12
  3. data/stats.data_size.csv +2 -2
  4. data/stats.entity_distribution.png +2 -2
  5. data/stats.predicate_distribution.png +2 -2
  6. data/stats.predicate_size.csv +4 -4
  7. data/t_rex.filter.min_entity_16_max_predicate_100.jsonl +0 -3
  8. data/t_rex.filter.min_entity_16_max_predicate_50.jsonl +0 -3
  9. data/t_rex.filter.min_entity_4_max_predicate_10.jsonl +0 -3
  10. data/t_rex.filter.min_entity_4_max_predicate_100.jsonl +0 -3
  11. data/t_rex.filter.min_entity_4_max_predicate_25.jsonl +0 -3
  12. data/t_rex.filter.min_entity_4_max_predicate_50.jsonl +0 -3
  13. data/t_rex.filter.min_entity_8_max_predicate_10.jsonl +0 -3
  14. data/t_rex.filter.min_entity_8_max_predicate_100.jsonl +0 -3
  15. data/t_rex.filter.min_entity_8_max_predicate_25.jsonl +0 -3
  16. data/t_rex.filter.min_entity_8_max_predicate_50.jsonl +0 -3
  17. data/{t_rex.filter.min_entity_12_max_predicate_10.jsonl → t_rex.filter_unified.min_entity_12_max_predicate_10.jsonl} +2 -2
  18. data/{t_rex.filter.min_entity_16_max_predicate_10.jsonl → t_rex.filter_unified.min_entity_12_max_predicate_100.jsonl} +2 -2
  19. data/{t_rex.filter.min_entity_12_max_predicate_25.jsonl → t_rex.filter_unified.min_entity_12_max_predicate_25.jsonl} +2 -2
  20. data/{t_rex.filter.min_entity_16_max_predicate_25.jsonl → t_rex.filter_unified.min_entity_12_max_predicate_50.jsonl} +2 -2
  21. data/t_rex.filter_unified.min_entity_16_max_predicate_10.jsonl +3 -0
  22. data/t_rex.filter_unified.min_entity_16_max_predicate_100.jsonl +3 -0
  23. data/t_rex.filter_unified.min_entity_16_max_predicate_25.jsonl +3 -0
  24. data/t_rex.filter_unified.min_entity_16_max_predicate_50.jsonl +3 -0
  25. data/t_rex.filter_unified.min_entity_4_max_predicate_10.jsonl +3 -0
  26. data/{t_rex.filter.min_entity_12_max_predicate_100.jsonl → t_rex.filter_unified.min_entity_4_max_predicate_100.jsonl} +2 -2
  27. data/t_rex.filter_unified.min_entity_4_max_predicate_25.jsonl +3 -0
  28. data/t_rex.filter_unified.min_entity_4_max_predicate_50.jsonl +3 -0
  29. data/t_rex.filter_unified.min_entity_8_max_predicate_10.jsonl +3 -0
  30. data/{t_rex.filter.min_entity_12_max_predicate_50.jsonl → t_rex.filter_unified.min_entity_8_max_predicate_100.jsonl} +2 -2
  31. data/t_rex.filter_unified.min_entity_8_max_predicate_25.jsonl +3 -0
  32. data/t_rex.filter_unified.min_entity_8_max_predicate_50.jsonl +3 -0
  33. filtering_purify.py +2 -2
  34. t_rex.py +6 -0
.gitattributes CHANGED
@@ -80,3 +80,19 @@ data/t_rex.filter.min_entity_16_max_predicate_100.jsonl filter=lfs diff=lfs merg
80
  data/t_rex.filter.min_entity_4_max_predicate_25.jsonl filter=lfs diff=lfs merge=lfs -text
81
  data/t_rex.filter.min_entity_8_max_predicate_25.jsonl filter=lfs diff=lfs merge=lfs -text
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  data/t_rex.filter_unified.jsonl filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  data/t_rex.filter.min_entity_4_max_predicate_25.jsonl filter=lfs diff=lfs merge=lfs -text
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  data/t_rex.filter.min_entity_8_max_predicate_25.jsonl filter=lfs diff=lfs merge=lfs -text
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  data/t_rex.filter_unified.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/t_rex.filter_unified.min_entity_16_max_predicate_25.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/t_rex.filter_unified.min_entity_8_max_predicate_100.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/t_rex.filter_unified.min_entity_12_max_predicate_25.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/t_rex.filter_unified.min_entity_8_max_predicate_10.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/t_rex.filter_unified.min_entity_8_max_predicate_25.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/t_rex.filter_unified.min_entity_8_max_predicate_50.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/t_rex.filter_unified.min_entity_4_max_predicate_50.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/t_rex.filter_unified.min_entity_16_max_predicate_10.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/t_rex.filter_unified.min_entity_16_max_predicate_100.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/t_rex.filter_unified.min_entity_16_max_predicate_50.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/t_rex.filter_unified.min_entity_4_max_predicate_10.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/t_rex.filter_unified.min_entity_4_max_predicate_25.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/t_rex.filter_unified.min_entity_12_max_predicate_10.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/t_rex.filter_unified.min_entity_12_max_predicate_100.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/t_rex.filter_unified.min_entity_12_max_predicate_50.jsonl filter=lfs diff=lfs merge=lfs -text
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+ data/t_rex.filter_unified.min_entity_4_max_predicate_100.jsonl filter=lfs diff=lfs merge=lfs -text
README.md CHANGED
@@ -21,7 +21,7 @@ We split the raw T-REX dataset into train/validation/test split by the ratio of
21
  ### Filtering to Remove Noise
22
 
23
  We apply filtering to keep triples with alpha-numeric subject and object, as well as triples with at least either of subject or object is a named-entity.
24
- After the filtering, we manually remove too vague and noisy predicate, and unify same predicates with different names.
25
 
26
  | Dataset | Raw | Filter | Unification |
27
  |----------:|----------:|----------:|--------------:|
@@ -38,20 +38,21 @@ we choose top-`max predicate` triples based on the frequency of the subject and
38
 
39
  - number of triples in each configuration
40
 
41
- | min entity / max predicate | 10 | 25 | 50 | 100 |
42
- |-----------------------------:|-----:|-----:|------:|------:|
43
- | 4 | 4,501 | 9,245 | 15,196 | 23,936 |
44
- | 8 | 3,557 | 7,291 | 11,804 | 18,699 |
45
- | 12 | 3,132 | 6,346 | 10,155 | 16,115 |
46
- | 16 | 2,769 | 5,559 | 9,014 | 14,499 |
47
 
 
48
 
49
- | min entity | predicate |
50
  |-------------:|-----:|
51
- | 4 | 516 |
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- | 8 | 409 |
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- | 12 | 366 |
54
- | 16 | 321 |
55
 
56
  - distribution of entities
57
 
 
21
  ### Filtering to Remove Noise
22
 
23
  We apply filtering to keep triples with alpha-numeric subject and object, as well as triples with at least either of subject or object is a named-entity.
24
+ After the filtering, we manually remove too vague and noisy predicate, and unify same predicates with different names (see the annotation [here](https://huggingface.co/datasets/relbert/t_rex/raw/main/predicate_manual_check.csv)).
25
 
26
  | Dataset | Raw | Filter | Unification |
27
  |----------:|----------:|----------:|--------------:|
 
38
 
39
  - number of triples in each configuration
40
 
41
+ | min entity / max predicate | 10 | 25 | 50 | 100 |
42
+ |-----------------------------:|-----:|-----:|-----:|------:|
43
+ | 4 | 1781 | 3961 | 6717 | 10768 |
44
+ | 8 | 1522 | 3216 | 5286 | 8443 |
45
+ | 12 | 1301 | 2720 | 4505 | 7191 |
46
+ | 16 | 1120 | 2389 | 3994 | 6481 |
47
 
48
+ - number of predicates in different min entity size
49
 
50
+ | min entity | 10 |
51
  |-------------:|-----:|
52
+ | 4 | 193 |
53
+ | 8 | 168 |
54
+ | 12 | 146 |
55
+ | 16 | 123 |
56
 
57
  - distribution of entities
58
 
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@@ -19,7 +19,7 @@ assert len(parameters_max_p_freq) == 4
19
  sns.set_theme(style="whitegrid")
20
 
21
  # load filtered data
22
- with open(f"data/t_rex.filter.jsonl") as f:
23
  data = Dataset.from_list([json.loads(i) for i in f.read().split('\n') if len(i) > 0])
24
  df_main = data.to_pandas()
25
 
@@ -99,7 +99,7 @@ if __name__ == '__main__':
99
  data_size_full.append(data_size)
100
  config.append([min_e_freq, max_p_freq])
101
  # save data
102
- with open(f"data/t_rex.filter.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.jsonl", 'w') as f:
103
  f.write('\n'.join([json.dumps(i) for i in new_data]))
104
 
105
  # check statistics
 
19
  sns.set_theme(style="whitegrid")
20
 
21
  # load filtered data
22
+ with open(f"data/t_rex.filter_unified.jsonl") as f:
23
  data = Dataset.from_list([json.loads(i) for i in f.read().split('\n') if len(i) > 0])
24
  df_main = data.to_pandas()
25
 
 
99
  data_size_full.append(data_size)
100
  config.append([min_e_freq, max_p_freq])
101
  # save data
102
+ with open(f"data/t_rex.filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.jsonl", 'w') as f:
103
  f.write('\n'.join([json.dumps(i) for i in new_data]))
104
 
105
  # check statistics
t_rex.py CHANGED
@@ -1,4 +1,6 @@
1
  import json
 
 
2
  import datasets
3
 
4
 
@@ -17,7 +19,11 @@ _CITATION = """
17
 
18
  _HOME_PAGE = "https://github.com/asahi417/relbert"
19
  _URL = f'https://huggingface.co/datasets/relbert/{_NAME}/resolve/main/data'
 
 
 
20
  _TYPES = ["raw", "filter", "filter_unified"]
 
21
  _NON_SPLITS = ["raw", "filter", "filter_unified"]
22
  _URLS = {i: {str(datasets.Split.TRAIN): [f'{_URL}/t_rex.{i}.jsonl']} if i in _NON_SPLITS else {
23
  str(datasets.Split.TRAIN): [f'{_URL}/t_rex.{i}.train.jsonl'],
 
1
  import json
2
+ from itertools import product
3
+
4
  import datasets
5
 
6
 
 
19
 
20
  _HOME_PAGE = "https://github.com/asahi417/relbert"
21
  _URL = f'https://huggingface.co/datasets/relbert/{_NAME}/resolve/main/data'
22
+ MIN_ENTITY_FREQ = [4, 8, 12, 16]
23
+ MAX_PREDICATE_FREQ = [100, 50, 25, 10]
24
+
25
  _TYPES = ["raw", "filter", "filter_unified"]
26
+ # _TYPES += [f"filter_unified.min_entity_{a}_max_predicate_{b}" for a, b in product(MIN_ENTITY_FREQ, MAX_PREDICATE_FREQ)]
27
  _NON_SPLITS = ["raw", "filter", "filter_unified"]
28
  _URLS = {i: {str(datasets.Split.TRAIN): [f'{_URL}/t_rex.{i}.jsonl']} if i in _NON_SPLITS else {
29
  str(datasets.Split.TRAIN): [f'{_URL}/t_rex.{i}.train.jsonl'],