init

.gitattributes

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 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
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+data/train.jsonl filter=lfs diff=lfs merge=lfs -text
+data/valid.jsonl filter=lfs diff=lfs merge=lfs -text

.gitignore

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+cache

README.md

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+---
+language:
+- en
+license:
+- other
+multilinguality:
+- monolingual
+pretty_name: SemEval2012 task 2 Relational Similarity
+---
+# Dataset Card for "relbert/semeval2012_relational_similarity"
+## Dataset Description
+- **Repository:** [RelBERT](https://github.com/asahi417/relbert)
+- **Paper:** [https://aclanthology.org/S12-1047/](https://aclanthology.org/S12-1047/)
+- **Dataset:** SemEval2012: Relational Similarity
+
+### Dataset Summary
+Relational similarity dataset from [SemEval2012 task 2](https://aclanthology.org/S12-1047/), compiled to fine-tune [RelBERT](https://github.com/asahi417/relbert) model.
+The dataset contains a list of positive and negative word pair from 89 pre-defined relations.
+The relation types are constructed on top of following 10 parent relation types.  
+```shell
+{
+    1: "Class Inclusion",  # Hypernym
+    2: "Part-Whole",  # Meronym, Substance Meronym
+    3: "Similar",  # Synonym, Co-hypornym
+    4: "Contrast",  # Antonym
+    5: "Attribute",  # Attribute, Event
+    6: "Non Attribute",
+    7: "Case Relation",
+    8: "Cause-Purpose",
+    9: "Space-Time",
+    10: "Representation"
+}
+```
+Each of the parent relation is further grouped into child relation types where the definition can be found [here](https://drive.google.com/file/d/0BzcZKTSeYL8VenY0QkVpZVpxYnc/view?resourcekey=0-ZP-UARfJj39PcLroibHPHw).
+
+
+## Dataset Structure
+### Data Instances
+An example of `train` looks as follows.
+```
+{
+  'relation_type': '8d',
+  'positives': [ [ "breathe", "live" ], [ "study", "learn" ], [ "speak", "communicate" ], ... ]
+  'negatives': [ [ "starving", "hungry" ], [ "clean", "bathe" ], [ "hungry", "starving" ], ... ] 
+}
+```
+
+### Data Splits
+|  name   |train|validation|
+|---------|----:|---------:|
+|semeval2012_relational_similarity| 89 |      89|
+
+
+### Citation Information
+```
+@inproceedings{jurgens-etal-2012-semeval,
+    title = "{S}em{E}val-2012 Task 2: Measuring Degrees of Relational Similarity",
+    author = "Jurgens, David  and
+      Mohammad, Saif  and
+      Turney, Peter  and
+      Holyoak, Keith",
+    booktitle = "*{SEM} 2012: The First Joint Conference on Lexical and Computational Semantics {--} Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation ({S}em{E}val 2012)",
+    month = "7-8 " # jun,
+    year = "2012",
+    address = "Montr{\'e}al, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/S12-1047",
+    pages = "356--364",
+}
+```

data/train.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:82446ec2cb00f4d66ad37b699a404b5980d366488e49b23a9f55e3dcc3f604af
+size 32277

data/valid.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7093b17813ae66c8206d9dcf1a6757376db8811956ee350ea1669a4b2313725b
+size 11205

process.py

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+import json
+import os
+import tarfile
+import zipfile
+import gzip
+import requests
+import gdown
+from glob import glob
+
+
+def wget(url, cache_dir: str = './cache', gdrive_filename: str = None):
+    """ wget and uncompress data_iterator """
+    path = _wget(url, cache_dir, gdrive_filename=gdrive_filename)
+    if path.endswith('.tar.gz') or path.endswith('.tgz') or path.endswith('.tar'):
+        if path.endswith('.tar'):
+            tar = tarfile.open(path)
+        else:
+            tar = tarfile.open(path, "r:gz")
+        tar.extractall(cache_dir)
+        tar.close()
+        os.remove(path)
+    elif path.endswith('.zip'):
+        with zipfile.ZipFile(path, 'r') as zip_ref:
+            zip_ref.extractall(cache_dir)
+        os.remove(path)
+    elif path.endswith('.gz'):
+        with gzip.open(path, 'rb') as f:
+            with open(path.replace('.gz', ''), 'wb') as f_write:
+                f_write.write(f.read())
+        os.remove(path)
+
+
+def _wget(url: str, cache_dir, gdrive_filename: str = None):
+    """ get data from web """
+    os.makedirs(cache_dir, exist_ok=True)
+    if url.startswith('https://drive.google.com'):
+        assert gdrive_filename is not None, 'please provide fileaname for gdrive download'
+        return gdown.download(url, f'{cache_dir}/{gdrive_filename}', quiet=False)
+    filename = os.path.basename(url)
+    with open(f'{cache_dir}/{filename}', "wb") as f:
+        r = requests.get(url)
+        f.write(r.content)
+    return f'{cache_dir}/{filename}'
+
+
+def get_data(n_sample: int = 10, v_rate: float = 0.2, n_sample_max: int = 10):
+    assert n_sample <= n_sample_max
+    cache_dir = 'cache'
+    os.makedirs(cache_dir, exist_ok=True)
+
+    path_answer = f'{cache_dir}/Phase2Answers'
+    path_scale = f'{cache_dir}/Phase2AnswersScaled'
+    url = 'https://drive.google.com/u/0/uc?id=0BzcZKTSeYL8VYWtHVmxUR3FyUmc&export=download'
+    filename = 'SemEval-2012-Platinum-Ratings.tar.gz'
+    if not (os.path.exists(path_scale) and os.path.exists(path_answer)):
+        wget(url, gdrive_filename=filename, cache_dir=cache_dir)
+    files_answer = [os.path.basename(i) for i in glob(f'{path_answer}/*.txt')]
+    files_scale = [os.path.basename(i) for i in glob(f'{path_scale}/*.txt')]
+    assert files_answer == files_scale, f'files are not matched: {files_scale} vs {files_answer}'
+    all_positive_v = {}
+    all_negative_v = {}
+    all_positive_t = {}
+    all_negative_t = {}
+    for i in files_scale:
+        relation_id = i.split('-')[-1].replace('.txt', '')
+        with open(f'{path_answer}/{i}', 'r') as f:
+            lines_answer = [l.replace('"', '').split('\t') for l in f.read().split('\n') if not l.startswith('#') and len(l)]
+            relation_type = list(set(list(zip(*lines_answer))[-1]))
+            assert len(relation_type) == 1, relation_type
+        with open(f'{path_scale}/{i}', 'r') as f:
+            lines_scale = [[float(l[:5]), l[6:].replace('"', '')] for l in f.read().split('\n')
+                           if not l.startswith('#') and len(l)]
+            lines_scale = sorted(lines_scale, key=lambda x: x[0])
+            _negative = [tuple(i.split(':')) for i in list(zip(*list(filter(lambda x: x[0] < 0, lines_scale[:n_sample_max]))))[1]]
+            _positive = [tuple(i.split(':')) for i in list(zip(*list(filter(lambda x: x[0] > 0, lines_scale[-n_sample_max:]))))[1]]
+            v_negative = _negative[::int(len(_negative) * (1 - v_rate))]
+            v_positive = _positive[::int(len(_positive) * (1 - v_rate))]
+            t_negative = [i for i in _negative if i not in v_negative]
+            t_positive = [i for i in _positive if i not in v_positive]
+
+            all_negative_v[relation_id] = v_negative
+            all_positive_v[relation_id] = v_positive
+            all_negative_t[relation_id] = t_negative[:n_sample]
+            all_positive_t[relation_id] = t_positive[-n_sample:]
+
+    return (all_positive_t, all_negative_t), (all_positive_v, all_negative_v)
+
+
+if __name__ == '__main__':
+    (all_positive_t, all_negative_t), (all_positive_v, all_negative_v) = get_data(n_sample=10, v_rate=0.2, n_sample_max=10)
+    os.makedirs('data', exist_ok=True)
+
+    keys = all_positive_t.keys()
+    with open("data/train.jsonl", "w") as f:
+        for k in sorted(keys):
+            f.write(json.dumps({"relation_type": k, "positives": all_positive_t[k], "negatives": all_negative_t[k]}))
+            f.write("\n")
+
+    keys = all_positive_v.keys()
+    with open("data/valid.jsonl", "w") as f:
+        for k in sorted(keys):
+            f.write(json.dumps({"relation_type": k, "positives": all_positive_v[k], "negatives": all_negative_v[k]}))
+            f.write("\n")

semeval2012_relational_similarity.py

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+import json
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+_DESCRIPTION = """[SemEVAL 2012 task 2: Relational Similarity](https://aclanthology.org/S12-1047/)"""
+_NAME = "semeval2012_relational_similarity"
+_VERSION = "0.0.0"
+_CITATION = """
+@inproceedings{jurgens-etal-2012-semeval,
+    title = "{S}em{E}val-2012 Task 2: Measuring Degrees of Relational Similarity",
+    author = "Jurgens, David  and
+      Mohammad, Saif  and
+      Turney, Peter  and
+      Holyoak, Keith",
+    booktitle = "*{SEM} 2012: The First Joint Conference on Lexical and Computational Semantics {--} Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation ({S}em{E}val 2012)",
+    month = "7-8 " # jun,
+    year = "2012",
+    address = "Montr{\'e}al, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/S12-1047",
+    pages = "356--364",
+}
+"""
+
+_HOME_PAGE = "https://github.com/asahi417/relbert"
+_URL = f'https://huggingface.co/datasets/relbert/{_NAME}/resolve/main/dataset'
+_URLS = {
+    str(datasets.Split.TRAIN): [f'{_URL}/train.jsonl'],
+    str(datasets.Split.VALIDATION): [f'{_URL}/valid.jsonl'],
+}
+
+
+class SemEVAL2012RelationalSimilarityConfig(datasets.BuilderConfig):
+    """BuilderConfig"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(SemEVAL2012RelationalSimilarityConfig, self).__init__(**kwargs)
+
+
+class SemEVAL2012RelationalSimilarity(datasets.GeneratorBasedBuilder):
+    """Dataset."""
+
+    BUILDER_CONFIGS = [
+        SemEVAL2012RelationalSimilarityConfig(name=_NAME, version=datasets.Version(_VERSION), description=_DESCRIPTION)
+    ]
+
+    def _split_generators(self, dl_manager):
+        downloaded_file = dl_manager.download_and_extract(_URLS)
+        return [datasets.SplitGenerator(name=i, gen_kwargs={"filepaths": downloaded_file[str(i)]})
+                for i in [datasets.Split.TRAIN, datasets.Split.VALIDATION]]
+
+    def _generate_examples(self, filepaths):
+        _key = 0
+        for filepath in filepaths:
+            logger.info(f"generating examples from = {filepath}")
+            with open(filepath, encoding="utf-8") as f:
+                _list = [i for i in f.read().split('\n') if len(i) > 0]
+                for i in _list:
+                    data = json.loads(i)
+                    yield _key, data
+                    _key += 1
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "relation_type": datasets.Value("string"),
+                    "positives": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
+                    "negatives": datasets.Sequence(datasets.Sequence(datasets.Value("string"))),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOME_PAGE,
+            citation=_CITATION,
+        )