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fix readme

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  1. README.md +146 -7
  2. stats.py +21 -5
README.md CHANGED
@@ -7,27 +7,166 @@ multilinguality:
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  - monolingual
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  size_categories:
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  - n<1K
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- pretty_name: nell
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  ---
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  # Dataset Card for "relbert/nell"
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  ## Dataset Description
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  - **Repository:** [https://github.com/xwhan/One-shot-Relational-Learning](https://github.com/xwhan/One-shot-Relational-Learning)
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  - **Paper:** [https://aclanthology.org/D18-1223/](https://aclanthology.org/D18-1223/)
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- - **Dataset:** Few-shots link prediction
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  ### Dataset Summary
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  This is NELL-ONE dataset for the few-shots link prediction proposed in [https://aclanthology.org/D18-1223/](https://aclanthology.org/D18-1223/).
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  Please see [NELL paper](https://www.cs.cmu.edu/~tom/pubs/NELL_aaai15.pdf) to know more about the original dataset.
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- | train | validation | test |
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- |------:|-----------:|-----:|
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- |8,526 | 1,004 | 2,158 |
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- |5,498 | 878 | 1,352 |
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  ## Dataset Structure
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- ### Data Instances
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  An example of `test` looks as follows.
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  ```
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  {
 
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  - monolingual
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  size_categories:
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  - n<1K
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+ pretty_name: relbert/nell
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  ---
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  # Dataset Card for "relbert/nell"
14
  ## Dataset Description
15
  - **Repository:** [https://github.com/xwhan/One-shot-Relational-Learning](https://github.com/xwhan/One-shot-Relational-Learning)
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  - **Paper:** [https://aclanthology.org/D18-1223/](https://aclanthology.org/D18-1223/)
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+ - **Dataset:** Never Ending Language Learner (NELL) dataset for one-shot link prediction.
18
 
19
  ### Dataset Summary
20
  This is NELL-ONE dataset for the few-shots link prediction proposed in [https://aclanthology.org/D18-1223/](https://aclanthology.org/D18-1223/).
21
  Please see [NELL paper](https://www.cs.cmu.edu/~tom/pubs/NELL_aaai15.pdf) to know more about the original dataset.
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+ - Number of instances
 
 
 
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+ | | train | validation | test |
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+ |:----------------|--------:|-------------:|-------:|
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+ | number of pairs | 5498 | 878 | 1352 |
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+
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+ - Number of pairs in each relation type
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+
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+ | | number of pairs (train) | number of pairs (validation) | number of pairs (test) |
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+ |:---------------------------------------------------|--------------------------:|-------------------------------:|-------------------------:|
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+ | concept:airportincity | 210 | 0 | 0 |
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+ | concept:athleteledsportsteam | 424 | 0 | 0 |
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+ | concept:automobilemakercardealersinstateorprovince | 78 | 0 | 0 |
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+ | concept:bankboughtbank | 58 | 0 | 0 |
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+ | concept:ceoof | 271 | 0 | 0 |
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+ | concept:cityradiostation | 99 | 0 | 0 |
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+ | concept:citytelevisionstation | 316 | 0 | 0 |
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+ | concept:countriessuchascountries | 100 | 0 | 0 |
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+ | concept:countrycapital | 211 | 0 | 0 |
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+ | concept:countryhascitizen | 182 | 0 | 0 |
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+ | concept:countryoforganizationheadquarters | 166 | 0 | 0 |
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+ | concept:countrystates | 169 | 0 | 0 |
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+ | concept:drugpossiblytreatsphysiologicalcondition | 91 | 0 | 0 |
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+ | concept:fatherofperson | 108 | 0 | 0 |
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+ | concept:fooddecreasestheriskofdisease | 1 | 0 | 0 |
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+ | concept:hasofficeincountry | 283 | 0 | 0 |
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+ | concept:leaguecoaches | 71 | 0 | 0 |
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+ | concept:leaguestadiums | 279 | 0 | 0 |
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+ | concept:musicartistmusician | 118 | 0 | 0 |
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+ | concept:musicgenressuchasmusicgenres | 107 | 0 | 0 |
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+ | concept:organizationnamehasacronym | 61 | 0 | 0 |
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+ | concept:personalsoknownas | 78 | 0 | 0 |
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+ | concept:personleadsgeopoliticalorganization | 120 | 0 | 0 |
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+ | concept:personmovedtostateorprovince | 225 | 0 | 0 |
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+ | concept:politicianrepresentslocation | 258 | 0 | 0 |
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+ | concept:politicianusholdsoffice | 216 | 0 | 0 |
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+ | concept:statehascapital | 151 | 0 | 0 |
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+ | concept:stateorprovinceoforganizationheadquarters | 118 | 0 | 0 |
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+ | concept:teamhomestadium | 138 | 0 | 0 |
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+ | concept:teamplaysincity | 338 | 0 | 0 |
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+ | concept:topmemberoforganization | 354 | 0 | 0 |
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+ | concept:wifeof | 99 | 0 | 0 |
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+ | concept:bankbankincountry | 0 | 229 | 0 |
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+ | concept:cityalsoknownas | 0 | 356 | 0 |
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+ | concept:parentofperson | 0 | 217 | 0 |
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+ | concept:politicalgroupofpoliticianus | 0 | 76 | 0 |
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+ | concept:automobilemakerdealersincity | 0 | 0 | 177 |
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+ | concept:automobilemakerdealersincountry | 0 | 0 | 96 |
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+ | concept:geopoliticallocationresidenceofpersion | 0 | 0 | 143 |
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+ | concept:politicianusendorsespoliticianus | 0 | 0 | 386 |
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+ | concept:producedby | 0 | 0 | 209 |
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+ | concept:teamcoach | 0 | 0 | 341 |
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+
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+ - Number of entity types
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+
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+ | | head (train) | tail (train) | head (validation) | tail (validation) | head (test) | tail (test) |
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+ |:-------------------------|---------------:|---------------:|--------------------:|--------------------:|--------------:|--------------:|
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+ | actor | 6 | 2 | 0 | 0 | 0 | 0 |
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+ | airport | 152 | 0 | 0 | 0 | 0 | 0 |
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+ | astronaut | 4 | 0 | 0 | 1 | 0 | 1 |
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+ | athlete | 353 | 21 | 1 | 2 | 0 | 59 |
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+ | attraction | 4 | 1 | 0 | 0 | 0 | 0 |
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+ | automobilemaker | 131 | 29 | 0 | 0 | 273 | 54 |
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+ | bank | 109 | 126 | 144 | 0 | 0 | 0 |
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+ | biotechcompany | 14 | 80 | 0 | 0 | 0 | 10 |
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+ | building | 4 | 0 | 0 | 0 | 0 | 0 |
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+ | celebrity | 6 | 5 | 0 | 0 | 4 | 2 |
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+ | ceo | 423 | 0 | 0 | 0 | 0 | 0 |
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+ | city | 342 | 852 | 316 | 316 | 42 | 161 |
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+ | coach | 29 | 61 | 0 | 3 | 0 | 245 |
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+ | comedian | 1 | 0 | 0 | 0 | 0 | 0 |
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+ | company | 76 | 549 | 1 | 0 | 1 | 144 |
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+ | country | 755 | 455 | 0 | 197 | 27 | 91 |
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+ | county | 36 | 39 | 11 | 11 | 10 | 4 |
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+ | creditunion | 1 | 0 | 0 | 0 | 0 | 0 |
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+ | criminal | 3 | 0 | 1 | 0 | 0 | 1 |
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+ | director | 2 | 0 | 0 | 0 | 0 | 1 |
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+ | drug | 91 | 0 | 0 | 0 | 1 | 0 |
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+ | female | 116 | 8 | 38 | 9 | 3 | 3 |
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+ | geopoliticallocation | 184 | 112 | 96 | 29 | 24 | 8 |
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+ | geopoliticalorganization | 28 | 68 | 8 | 21 | 1 | 7 |
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+ | governmentorganization | 25 | 95 | 74 | 0 | 0 | 0 |
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+ | island | 15 | 4 | 4 | 6 | 1 | 0 |
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+ | journalist | 4 | 0 | 0 | 0 | 0 | 1 |
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+ | male | 132 | 78 | 37 | 52 | 1 | 5 |
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+ | model | 2 | 0 | 0 | 0 | 0 | 0 |
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+ | monarch | 4 | 3 | 4 | 1 | 0 | 0 |
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+ | museum | 1 | 5 | 0 | 0 | 0 | 0 |
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+ | musicartist | 118 | 5 | 0 | 0 | 0 | 0 |
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+ | musicgenre | 107 | 107 | 0 | 0 | 0 | 0 |
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+ | musician | 5 | 124 | 0 | 0 | 0 | 0 |
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+ | newspaper | 3 | 2 | 0 | 0 | 0 | 0 |
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+ | organization | 23 | 86 | 1 | 1 | 32 | 2 |
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+ | person | 350 | 256 | 116 | 131 | 0 | 96 |
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+ | personafrica | 1 | 3 | 0 | 0 | 0 | 0 |
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+ | personasia | 1 | 3 | 0 | 0 | 0 | 0 |
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+ | personaustralia | 38 | 5 | 0 | 0 | 0 | 5 |
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+ | personcanada | 19 | 14 | 0 | 0 | 0 | 0 |
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+ | personeurope | 9 | 7 | 14 | 4 | 0 | 1 |
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+ | personmexico | 57 | 14 | 0 | 0 | 0 | 20 |
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+ | personnorthamerica | 9 | 6 | 0 | 0 | 0 | 3 |
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+ | personsouthamerica | 1 | 1 | 0 | 17 | 0 | 0 |
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+ | personus | 41 | 21 | 2 | 0 | 1 | 6 |
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+ | planet | 1 | 0 | 0 | 0 | 0 | 1 |
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+ | politician | 107 | 5 | 0 | 1 | 23 | 58 |
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+ | politicianus | 408 | 12 | 3 | 71 | 352 | 360 |
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+ | politicsblog | 2 | 3 | 0 | 0 | 0 | 0 |
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+ | port | 7 | 0 | 0 | 0 | 0 | 0 |
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+ | professor | 7 | 2 | 0 | 0 | 1 | 0 |
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+ | publication | 1 | 21 | 0 | 0 | 0 | 0 |
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+ | recordlabel | 1 | 13 | 0 | 0 | 0 | 0 |
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+ | retailstore | 1 | 15 | 0 | 0 | 0 | 0 |
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+ | school | 54 | 1 | 0 | 0 | 11 | 0 |
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+ | scientist | 5 | 2 | 0 | 1 | 0 | 0 |
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+ | sportsleague | 356 | 12 | 0 | 0 | 0 | 0 |
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+ | sportsteam | 392 | 430 | 0 | 0 | 295 | 0 |
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+ | stateorprovince | 254 | 602 | 0 | 0 | 38 | 0 |
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+ | transportation | 36 | 2 | 0 | 0 | 0 | 0 |
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+ | university | 3 | 15 | 0 | 0 | 0 | 0 |
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+ | visualizablescene | 20 | 7 | 3 | 3 | 3 | 3 |
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+ | visualizablething | 1 | 1 | 1 | 1 | 0 | 0 |
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+ | website | 7 | 31 | 0 | 0 | 0 | 0 |
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+ | caf_ | 0 | 1 | 0 | 0 | 0 | 0 |
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+ | continent | 0 | 1 | 0 | 0 | 0 | 0 |
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+ | disease | 0 | 92 | 0 | 0 | 0 | 0 |
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+ | hotel | 0 | 1 | 0 | 0 | 0 | 0 |
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+ | magazine | 0 | 5 | 0 | 0 | 0 | 0 |
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+ | nongovorganization | 0 | 4 | 0 | 0 | 0 | 0 |
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+ | nonprofitorganization | 0 | 2 | 0 | 0 | 0 | 0 |
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+ | park | 0 | 1 | 0 | 0 | 0 | 0 |
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+ | petroleumrefiningcompany | 0 | 6 | 0 | 0 | 0 | 0 |
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+ | politicaloffice | 0 | 216 | 0 | 0 | 0 | 0 |
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+ | politicalparty | 0 | 6 | 2 | 0 | 0 | 0 |
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+ | radiostation | 0 | 93 | 0 | 0 | 0 | 0 |
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+ | river | 0 | 4 | 0 | 0 | 0 | 0 |
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+ | stadiumoreventvenue | 0 | 417 | 0 | 0 | 0 | 0 |
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+ | televisionnetwork | 0 | 1 | 0 | 0 | 0 | 0 |
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+ | televisionstation | 0 | 221 | 0 | 0 | 0 | 0 |
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+ | trainstation | 0 | 2 | 0 | 0 | 0 | 0 |
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+ | writer | 0 | 3 | 1 | 0 | 0 | 0 |
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+ | zoo | 0 | 1 | 0 | 0 | 0 | 0 |
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+ | automobilemodel | 0 | 0 | 0 | 0 | 100 | 0 |
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+ | product | 0 | 0 | 0 | 0 | 62 | 0 |
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+ | software | 0 | 0 | 0 | 0 | 42 | 0 |
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+ | videogame | 0 | 0 | 0 | 0 | 4 | 0 |
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169
  ## Dataset Structure
 
170
  An example of `test` looks as follows.
171
  ```
172
  {
stats.py CHANGED
@@ -3,11 +3,27 @@ from itertools import chain
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  import pandas as pd
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  from datasets import load_dataset
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6
- def get_stats(split):
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- data = load_dataset("relbert/nell", split=split)
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- df = data.to_pandas()
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10
 
11
- s = 'test'
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-
 
 
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  import pandas as pd
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  from datasets import load_dataset
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6
 
7
+ def get_stats():
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+ relation = []
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+ entity = []
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+ size = {}
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+ data = load_dataset("relbert/nell")
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+ splits = data.keys()
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+ for split in splits:
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+ df = data[split].to_pandas()
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+ size[split] = len(df)
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+ relation.append(df.groupby('relation')['head'].count().to_dict())
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+ entity += [df.groupby('head_type')['head'].count().to_dict(), df.groupby('tail_type')['tail'].count().to_dict()]
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+ relation = pd.DataFrame(relation, index=[f"number of pairs ({s})" for s in splits]).T
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+ relation = relation.fillna(0).astype(int)
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+ entity = pd.DataFrame(entity, index=list(chain(*[[f"head ({s})", f"tail ({s})"] for s in splits]))).T
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+ entity = entity.fillna(0).astype(int)
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+ size = pd.DataFrame([size], index=["number of pairs"])
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+ return relation, entity, size
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25
+ df_relation, df_entity, df_size = get_stats()
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+ print(f"\n- Number of instances\n\n {df_size.to_markdown()}")
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+ print(f"\n- Number of pairs in each relation type\n\n {df_relation.to_markdown()}")
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+ print(f"\n- Number of entity types\n\n {df_entity.to_markdown()}")
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