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constants.py

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+relations_map = {
+    'social_intercation': ['oEffect', 'oReact', 'oWant', 'xAttr', 'xEffect', 'xIntent', 'xNeed', 'xReact', 'xWant'],
+    'physical_entity'   : ['ObjectUse', 'AtLocation', 'Desires', 'NotDesires', 'HasProperty', 'MadeUpOf', 'CapableOf'],
+    'event_centered'    : ['isAfter', 'isBefore', 'HasSubEvent', 'HinderedBy', 'Causes', 'xReason', 'isFilledBy'],
+}
+
+social_intercation_2_descriptions = {
+    'xIntent': 'Why does X cause the event?',
+    'xNeed'  : 'What does X need to do before the event?',
+    'xAttr'  : 'How would X be described?',
+    'xEffect': 'What effects does the event have on X?',
+    'xWant'  : 'What would X likely want to do after the event?',
+    'xReact' : 'How does X feel after the event?',
+    'oEffect': 'What effects does the event have on others?',
+    'oReact' : 'How do others\' feel after the event?',
+    'oWant'  : 'What would others likely want to do after the event?',
+}
+
+physical_entity_2_descriptions = {
+    'ObjectUse'  : 'ObjectUse describes everyday affordances or uses of objects, and includes both typical and atypical uses.',
+    'AtLocation' : 'AtLocation is a spatial relation that describes the location in/on/at which an entity is likely to be found.',
+    'Desires'    : 'Desires are relations that deal with desires of sentient entities.',
+    'HasProperty': 'HasProperty usually describes entities\' general characteristics. In certain case, the relation can also map to descriptors that speak to the substance or value of items.',
+    'NotDesires' : 'Not desires are relations that deal with the absence of desires in sentient entities.',
+    'MadeUpOf'   : 'MadeUpOf describes a part, portion or makeup of an entity.',
+    'CapableOf'  : 'CapableOf describes abilities and capabilities of everyday living entities and natural entites that can exert a force. CapableOf includes general capabilities and specialized capabilities.',
+}
+
+event_centered_2_descriptions = {
+    'isAfter'    : 'isAfter introduces events that can follow an event.',
+    'HasSubEvent': 'HasSubEvent provides the internal structure of an event, each tail denoting a step within the larger head event.',
+    'isBefore'   : 'isBefore introduces events that can precede an event.',
+    'HinderedBy' : 'HinderedyBy introduces hindrances that obstruct the natural path to the achievement of a goal.',
+    'Causes'     : 'Causes specifically captures the causal relation between two events or entities.',
+    'xReason'    : 'xReason provides a post-fact explanation of the cause of an event.',
+    'isFilledBy' : 'isFilledBy provides a filler phrase for an event with a blank that is sensical and commonly acceptable for the event.',
+}

make_origin_dataset.py

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+from collections import Counter
+from typing import List
+
+import datasets
+import matplotlib.pyplot as plt
+import pandas as pd
+from constants import (event_centered_2_descriptions,
+                       physical_entity_2_descriptions, relations_map,
+                       social_intercation_2_descriptions)
+
+
+def show_bar(relation: List):
+    c = dict(Counter(relation))
+
+    keys = list(c.keys())
+    values = list(c.values())
+    plt.bar(keys, values)
+    plt.xticks(rotation=25, fontsize=8)
+    plt.yticks(fontsize=8)
+
+    plt.xlabel('relations')
+    plt.ylabel('numbers')
+    plt.title('relations analysis')
+
+    for i in range(len(keys)):
+        plt.text(i, values[i] + 10, str(values[i]), ha='center', fontsize=10)
+
+    plt.show()
+
+def read_file(data_path: str):
+    df = pd.read_csv(data_path, sep='\t', header=None)
+
+    df.columns = ['event', 'relation', 'tail']
+    print(df.head())
+
+    event = df['event'].tolist()
+    relation = df['relation'].tolist()
+    tail = df['tail'].tolist()
+
+    return event, relation, tail
+
+def make_base_dataset(event: List[str], relation: List[str], tail: List[str]):
+    new_event, new_relation, new_tail = [], [], []
+    knowledge_type = []
+    relation_description = []
+
+    prev_event, prev_relation, prev_tail = None, None, None
+
+    for i in range(len(event)):
+        if i > 0 and event[i] == prev_event and relation[i] == prev_relation:
+            new_tail[-1].extend( [tail[i]] )
+        else:
+            new_event.append(event[i])
+            new_relation.append(relation[i])
+            # insert knowledge type
+            relation_list = []
+            for r in list(relations_map.values()):
+                relation_list.extend(r)
+            if relation[i] not in relation_list:
+                raise ValueError(f'dont find match knowledge type named {relation[i]}, please check it!')
+            
+            for k, v in relations_map.items():
+                if relation[i] in v:
+                    knowledge_type.append(k)
+                    if k == 'social_intercation':
+                        relation_description.append(social_intercation_2_descriptions[relation[i]])
+                    elif k == 'physical_entity':
+                        relation_description.append(physical_entity_2_descriptions[relation[i]])
+                    elif k == 'event_centered':
+                        relation_description.append(event_centered_2_descriptions[relation[i]])
+                    else:
+                        raise KeyError(f"dont find match relation type named {relation[i]} in dict, please check it!")
+                    
+            new_tail.append( [tail[i]] )
+        prev_event, prev_relation, prev_tail = event[i], relation[i], tail[i]
+    
+    df = pd.DataFrame({
+        'knowledge_type': knowledge_type,
+        'event': new_event,
+        'relation': new_relation,
+        'relation_description': relation_description,
+        'tail': new_tail,
+    })
+
+    print(df.head())
+    return df
+
+def get_dataset(data_path: str):
+    event, relation, tail = read_file(data_path=data_path)
+    df = make_base_dataset(event=event, relation=relation, tail=tail)
+    dataset = datasets.Dataset.from_pandas(df, split='train')
+
+    print(dataset)
+    return dataset
+
+def upload_dataset(dataset, repo_id :str, access_token: str, private: bool):
+    dataset.push_to_hub(
+        repo_id = repo_id,
+        private = private,
+        token   = access_token,
+    )
+
+if __name__ == '__main__':
+    train_dataset = get_dataset('./dataset/train.tsv')
+    valid_dataset = get_dataset('./dataset/dev.tsv')
+    test_dataset = get_dataset('./dataset/test.tsv')
+    
+    dataset = datasets.DatasetDict({
+        'train': train_dataset,
+        'validation': valid_dataset,
+        'test': test_dataset
+    })
+
+    print(dataset)
+
+    upload_dataset(dataset, repo_id='Estwld/atomic2020-origin', private=False, access_token='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')