init
Browse files
README.md
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@@ -33,6 +33,93 @@ An example of `test` of `nell` looks as follows.
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}
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```
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### Citation Information
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```
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@inproceedings{xiong-etal-2018-one,
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}
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```
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## Statistics
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- Entity Types
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| entity_type | tail | head |
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|:-------------------------|-------:|-------:|
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| geopoliticallocation | 14 | 24 |
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| crustacean | 25 | 11 |
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| amphibian | 0 | 2 |
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| criminal | 1 | 0 |
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| arthropod | 41 | 32 |
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| videogame | 0 | 4 |
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| mlsoftware | 0 | 1 |
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| animal | 30 | 97 |
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| politician | 58 | 23 |
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| organization | 2 | 32 |
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| insect | 270 | 230 |
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| coach | 245 | 1 |
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| personmexico | 20 | 13 |
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| personus | 6 | 1 |
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| automobilemaker | 54 | 274 |
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| chemical | 0 | 1 |
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| astronaut | 1 | 0 |
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| company | 147 | 1 |
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| arachnid | 6 | 1 |
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| software | 0 | 42 |
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| biotechcompany | 11 | 0 |
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| mammal | 0 | 23 |
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| product | 0 | 62 |
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| sportsgame | 0 | 74 |
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| sport | 74 | 93 |
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| agriculturalproduct | 0 | 87 |
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| location | 2 | 0 |
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| hobby | 0 | 10 |
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| vehicle | 0 | 2 |
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| director | 1 | 0 |
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| planet | 1 | 0 |
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| athlete | 59 | 34 |
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| food | 0 | 4 |
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| grain | 0 | 2 |
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| politicianus | 360 | 352 |
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| automobilemodel | 0 | 100 |
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| coffeedrink | 0 | 11 |
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| city | 161 | 42 |
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| person | 96 | 14 |
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| bodypart | 69 | 0 |
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| female | 3 | 3 |
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| journalist | 1 | 0 |
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| geopoliticalorganization | 17 | 1 |
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| visualizablescene | 3 | 3 |
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| professor | 0 | 1 |
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| reptile | 0 | 4 |
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| drug | 0 | 1 |
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| stateorprovince | 0 | 38 |
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| beverage | 0 | 27 |
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| school | 0 | 11 |
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| county | 4 | 10 |
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| male | 5 | 3 |
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| celebrity | 2 | 4 |
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| country | 317 | 27 |
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| personaustralia | 5 | 4 |
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| sportsteam | 0 | 295 |
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| fruit | 0 | 1 |
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| island | 0 | 1 |
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| invertebrate | 43 | 14 |
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| personnorthamerica | 3 | 1 |
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| personeurope | 1 | 0 |
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| vegetable | 0 | 8 |
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| legume | 0 | 1 |
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- Relation Types
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| relation | relation |
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|:-----------------------------------------------|-----------:|
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| concept:agriculturalproductcamefromcountry | 140 |
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| concept:animalsuchasinvertebrate | 415 |
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| concept:athleteinjuredhisbodypart | 69 |
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| concept:automobilemakerdealersincity | 178 |
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| concept:automobilemakerdealersincountry | 96 |
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| concept:geopoliticallocationresidenceofpersion | 143 |
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| concept:politicianusendorsespoliticianus | 386 |
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| concept:producedby | 213 |
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| concept:sportschoolincountry | 103 |
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| concept:sportsgamesport | 74 |
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| concept:teamcoach | 341 |
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### Citation Information
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```
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@inproceedings{xiong-etal-2018-one,
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stats.py
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import pandas as pd
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from datasets import load_dataset
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data = load_dataset("fewshot_link_prediction", "nell", split='test')
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df = data.to_pandas()
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print("\nEntity Types")
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tail = df.groupby("tail_type")['relation'].count().to_dict()
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head = df.groupby("head_type")['relation'].count().to_dict()
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k = set(list(tail.keys()) + list(head.keys()))
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df_types = pd.DataFrame([{"entity_type": _k, "tail": tail[_k] if _k in tail else 0, "head": head[_k] if _k in head else 0} for _k in k])
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print(df_types.to_markdown(index=False))
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print("\nRelation Types")
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print(df.groupby("relation")['relation'].count().to_markdown())
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