fix readme

README.md

@@ -6,37 +6,70 @@ license:
 multilinguality:
 - monolingual
 size_categories:
-- 1K<n<10K
-pretty_name: ConceptNet with High Confidence
+- n<1K
+pretty_name: relbert/conceptnet
 ---
 # Dataset Card for "relbert/conceptnet"
+
 ## Dataset Description
 - **Repository:** [RelBERT](https://github.com/asahi417/relbert)
 - **Paper:** [https://home.ttic.edu/~kgimpel/commonsense.html](https://home.ttic.edu/~kgimpel/commonsense.html)
-- **Dataset:** High Confidence Subset of ConceptNet
+- **Dataset:** High Confidence Subset of ConceptNet for link prediction
 
 ### Dataset Summary
 The selected subset of ConceptNet used in [this work](https://home.ttic.edu/~kgimpel/commonsense.html).
 We removed `NotCapableOf` and `NotDesires` to keep the positive relation only.
 We consider the original test set as test set, dev1 as the training set, and dev2 as the validation set.
 
-| train | validation | test |
-|------:|----:|---------:|
-| 592 | 595 |      1189|
+- Number of instances
+
+ |                                 |   train |   validation |   test |
+|:--------------------------------|--------:|-------------:|-------:|
+| number of pairs                 |     592 |          595 |   1189 |
+| number of unique relation types |      18 |           22 |     21 |
+
+- Number of pairs in each relation type
+
+ |                  |   number of pairs (train) |   number of pairs (validation) |   number of pairs (test) |
+|:-----------------|--------------------------:|-------------------------------:|-------------------------:|
+| AtLocation       |                       133 |                             97 |                      250 |
+| CapableOf        |                        51 |                             73 |                      144 |
+| Causes           |                        26 |                             26 |                       45 |
+| CausesDesire     |                         4 |                             11 |                        5 |
+| Desires          |                         8 |                             12 |                        8 |
+| HasA             |                        26 |                             17 |                       41 |
+| HasFirstSubevent |                         1 |                              1 |                        1 |
+| HasLastSubevent  |                         2 |                              3 |                        0 |
+| HasPrerequisite  |                        59 |                             57 |                      109 |
+| HasProperty      |                        24 |                             39 |                       70 |
+| HasSubevent      |                        42 |                             40 |                       83 |
+| IsA              |                        99 |                             98 |                      211 |
+| MadeOf           |                         3 |                              7 |                       14 |
+| MotivatedByGoal  |                         6 |                             11 |                        8 |
+| NotMadeOf        |                         1 |                              0 |                        0 |
+| PartOf           |                        12 |                              7 |                       22 |
+| ReceivesAction   |                         7 |                              8 |                       11 |
+| UsedFor          |                        88 |                             81 |                      161 |
+| CreatedBy        |                         0 |                              1 |                        2 |
+| DefinedAs        |                         0 |                              2 |                        1 |
+| NotHasProperty   |                         0 |                              1 |                        1 |
+| NotIsA           |                         0 |                              1 |                        1 |
+| SymbolOf         |                         0 |                              2 |                        0 |
+| RelatedTo        |                         0 |                              0 |                        1 |
 
 
 ### Data Instances
 An example of `train` looks as follows.
-```
+```shell
 {
-    "relation_type": "AtLocation",
-    "positives": [["fish", "water"], ["cloud", "sky"], ["child", "school"], ... ],
-    "negatives": [["pen", "write"], ["sex", "fun"], ["soccer", "sport"], ["fish", "school"], ... ]
+  "relation": "IsA",
+  "head": "baseball",
+  "tail": "sport"
 }
 ```
 
 
-### Citation Information
+## Citation Information
 ```
 @InProceedings{P16-1137,
   author = 	"Li, Xiang

conceptnet.py

@@ -29,7 +29,7 @@ year = {2016}
 """
 
 _HOME_PAGE = "https://github.com/asahi417/relbert"
-_URL = f'https://huggingface.co/datasets/relbert/{_NAME}/raw/main/dataset'
+_URL = f'https://huggingface.co/datasets/relbert/{_NAME}/raw/main/data'
 _URLS = {
     str(datasets.Split.TRAIN): [f'{_URL}/train.jsonl'],
     str(datasets.Split.VALIDATION): [f'{_URL}/valid.jsonl'],

get_stats.py

@@ -1,35 +0,0 @@
-import pandas as pd
-from datasets import load_dataset
-
-data = load_dataset('relbert/conceptnet_high_confidence_v2')
-stats = []
-for k in data.keys():
-    for i in data[k]:
-        stats.append({'relation_type': i['relation_type'], 'split': k, 'positives': len(i['positives']), 'negatives': len(i['negatives'])})
-df = pd.DataFrame(stats)
-df_train = df[df['split'] == 'train']
-df_valid = df[df['split'] == 'validation']
-stats = []
-for r in df['relation_type'].unique():
-    _df_t = df_train[df_train['relation_type'] == r]
-    _df_v = df_valid[df_valid['relation_type'] == r]
-    stats.append({
-        'relation_type': r,
-        'positive (train)': 0 if len(_df_t) == 0 else _df_t['positives'].values[0],
-        'negative (train)': 0 if len(_df_t) == 0 else _df_t['negatives'].values[0],
-        'positive (validation)': 0 if len(_df_v) == 0 else _df_v['positives'].values[0],
-        'negative (validation)': 0 if len(_df_v) == 0 else _df_v['negatives'].values[0],
-    })
-
-df = pd.DataFrame(stats).sort_values(by=['relation_type'])
-df.index = df.pop('relation_type')
-sum_pairs = df.sum(0)
-df = df.T
-df['SUM'] = sum_pairs
-df = df.T
-
-df.to_csv('stats.csv')
-with open('stats.md', 'w') as f:
-    f.write(df.to_markdown())
-
-

stats.py

@@ -0,0 +1,27 @@
+from itertools import chain
+
+import pandas as pd
+from datasets import load_dataset
+
+
+def get_stats():
+    relation = []
+    size = []
+    data = load_dataset("relbert/conceptnet")
+    splits = data.keys()
+    for split in splits:
+        df = data[split].to_pandas()
+        size.append({
+            "number of pairs": len(df),
+            "number of unique relation types": len(df["relation"].unique())
+        })
+        relation.append(df.groupby('relation')['head'].count().to_dict())
+    relation = pd.DataFrame(relation, index=[f"number of pairs ({s})" for s in splits]).T
+    relation = relation.fillna(0).astype(int)
+    size = pd.DataFrame(size, index=splits).T
+    return relation, size
+
+df_relation, df_size = get_stats()
+print(f"\n- Number of instances\n\n {df_size.to_markdown()}")
+print(f"\n- Number of pairs in each relation type\n\n {df_relation.to_markdown()}")
+