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  1. README.md +87 -0
  2. stats.py +17 -0
README.md CHANGED
@@ -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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+
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+ - Entity Types
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+
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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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+
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+ - Relation Types
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+
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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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+
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+
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  ### Citation Information
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  ```
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  @inproceedings{xiong-etal-2018-one,
stats.py ADDED
@@ -0,0 +1,17 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import pandas as pd
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+ from datasets import load_dataset
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+
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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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+
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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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+
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+ print("\nRelation Types")
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+ print(df.groupby("relation")['relation'].count().to_markdown())
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+
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+