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README.md
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- other
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multilinguality:
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- monolingual
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---
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# Dataset Card for "relbert/t_rex_relation_similarity"
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## Dataset Description
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- **Repository:** [
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- **Paper:** [https://aclanthology.org/L18-1544/](https://aclanthology.org/L18-1544/)
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- **Dataset:** T-REX
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## Dataset Summary
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This is the clean version of [T-REX](https://aclanthology.org/L18-1544/) converted into relation similarity dataset format.
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The original dataset is [`relbert/t_rex`](https://huggingface.co/datasets/relbert/t_rex).
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- statistics of the train/validation split
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| data | num of relation types (train) | average num of positive pairs (train) | average num of negative pairs (train) | num of relation types (validation) | average num of positive pairs (validation) | average num of negative pairs (validation) |
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|:-------------------------------|--------------------------------:|----------------------------------------:|----------------------------------------:|-------------------------------------:|---------------------------------------------:|---------------------------------------------:|
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| min_entity_1_max_predicate_100 | 208 | 34 | 7041 | 133 | 6 | 781 |
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| min_entity_1_max_predicate_50 | 204 | 20 | 4111 | 113 | 4 | 455 |
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| min_entity_1_max_predicate_25 | 202 | 12 | 2346 | 71 | 3 | 259 |
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| min_entity_1_max_predicate_10 | 192 | 6 | 1128 | 25 | 2 | 125 |
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| min_entity_2_max_predicate_100 | 188 | 26 | 4847 | 107 | 5 | 537 |
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| min_entity_2_max_predicate_50 | 184 | 16 | 2986 | 85 | 3 | 331 |
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| min_entity_2_max_predicate_25 | 181 | 9 | 1702 | 51 | 3 | 188 |
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| min_entity_2_max_predicate_10 | 171 | 5 | 853 | 13 | 2 | 94 |
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| min_entity_3_max_predicate_100 | 166 | 22 | 3637 | 82 | 5 | 402 |
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| min_entity_3_max_predicate_50 | 157 | 15 | 2321 | 66 | 3 | 257 |
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| min_entity_3_max_predicate_25 | 156 | 9 | 1381 | 37 | 3 | 152 |
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| min_entity_3_max_predicate_10 | 148 | 4 | 684 | 17 | 2 | 75 |
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| min_entity_4_max_predicate_100 | 150 | 20 | 2975 | 73 | 4 | 329 |
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| min_entity_4_max_predicate_50 | 145 | 14 | 1975 | 56 | 3 | 219 |
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| min_entity_4_max_predicate_25 | 141 | 9 | 1212 | 34 | 3 | 133 |
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| min_entity_4_max_predicate_10 | 128 | 4 | 599 | 14 | 2 | 66 |
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- statistics of the test split
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| num of relation types (test) | average num of positive pairs (test) | average num of negative pairs (test) |
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|-------------------------------:|---------------------------------------:|---------------------------------------:|
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| 24 | 5 | 117 |
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## Dataset Structure
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### Data Instances
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An example looks as follows.
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{
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"relation_type": "[Airline] has a hub in [Location]",
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"positives": [["Korean Air", "Seoul"], ["Asiana Airlines", "Seoul"], ["Cathay Pacific", "Hong Kong"], ["Dragonair", "Hong Kong"], ["Qantas", "Singapore"], ["Air China", "Beijing"], ["Singapore Airlines", "Singapore"]],
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}
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```
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## Citation Information
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```
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@inproceedings{elsahar2018t,
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- other
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multilinguality:
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- monolingual
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size_categories:
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- n<1K
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pretty_name: T-REX for relational similarity
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---
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# Dataset Card for "relbert/t_rex_relation_similarity"
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## Dataset Description
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- **Repository:** [RelBERT](https://github.com/asahi417/relbert)
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- **Paper:** [https://aclanthology.org/L18-1544/](https://aclanthology.org/L18-1544/)
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- **Dataset:** T-REX for relational similarity
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## Dataset Summary
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This is the clean version of [T-REX](https://aclanthology.org/L18-1544/) converted into relation similarity dataset format.
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The original dataset is [`relbert/t_rex`](https://huggingface.co/datasets/relbert/t_rex).
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## Dataset Structure
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### Data Instances
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An example of `train` looks as follows.
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```shell
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{
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"relation_type": "[Airline] has a hub in [Location]",
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"positives": [["Korean Air", "Seoul"], ["Asiana Airlines", "Seoul"], ["Cathay Pacific", "Hong Kong"], ["Dragonair", "Hong Kong"], ["Qantas", "Singapore"], ["Air China", "Beijing"], ["Singapore Airlines", "Singapore"]],
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}
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```
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### Data Splits
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| name | train | validation | test |
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|:----------------------------------------------|--------:|-------------:|-------:|
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| filter_unified.min_entity_1_max_predicate_100 | 208 | 133 | 24 |
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| filter_unified.min_entity_1_max_predicate_50 | 204 | 113 | 24 |
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| filter_unified.min_entity_1_max_predicate_25 | 202 | 71 | 24 |
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| filter_unified.min_entity_1_max_predicate_10 | 192 | 25 | 24 |
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| filter_unified.min_entity_2_max_predicate_100 | 188 | 107 | 24 |
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| filter_unified.min_entity_2_max_predicate_50 | 184 | 85 | 24 |
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| filter_unified.min_entity_2_max_predicate_25 | 181 | 51 | 24 |
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| filter_unified.min_entity_2_max_predicate_10 | 171 | 13 | 24 |
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| filter_unified.min_entity_3_max_predicate_100 | 166 | 82 | 24 |
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| filter_unified.min_entity_3_max_predicate_50 | 157 | 66 | 24 |
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| filter_unified.min_entity_3_max_predicate_25 | 156 | 37 | 24 |
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| filter_unified.min_entity_3_max_predicate_10 | 148 | 17 | 24 |
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| filter_unified.min_entity_4_max_predicate_100 | 150 | 73 | 24 |
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| filter_unified.min_entity_4_max_predicate_50 | 145 | 56 | 24 |
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| filter_unified.min_entity_4_max_predicate_25 | 141 | 34 | 24 |
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| filter_unified.min_entity_4_max_predicate_10 | 128 | 14 | 24 |
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## Citation Information
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```
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@inproceedings{elsahar2018t,
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