Upload chinese_metaphor_dataset.py

chinese_metaphor_dataset.py

@@ -0,0 +1,85 @@
+import datasets
+# import pandas as pd
+import json
+
+_CITATION = """\
+@inproceedings{li-etal-2022-cm,
+    title = "{CM}-Gen: A Neural Framework for {C}hinese Metaphor Generation with Explicit Context Modelling",
+    author = "Li, Yucheng  and
+      Lin, Chenghua  and
+      Guerin, Frank",
+    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
+    month = oct,
+    year = "2022",
+    address = "Gyeongju, Republic of Korea",
+    publisher = "International Committee on Computational Linguistics",
+    url = "https://aclanthology.org/2022.coling-1.563",
+    pages = "6468--6479",
+}
+
+@misc{li-inlg-2022-nominal,
+  doi = {10.48550/ARXIV.2206.05195},
+  url = {https://arxiv.org/abs/2206.05195},
+  author = {Li, Yucheng and Lin, Chenghua and Geurin, Frank},
+  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  title = {Nominal Metaphor Generation with Multitask Learning},
+  publisher = {arXiv},
+  year = {2022},
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+"""
+
+_DESCRIPTION = """\
+Chinese Metaphor Corpus
+
+The first Chinese metaphor corpus serving both metaphor identification and generation. 
+首个中文比喻数据集，可以用于中文比喻识别与中文比喻生成。
+"""
+
+_HOMEPAGE = "https://github.com/liyucheng09/Metaphor_Generator"
+
+_URL = 'https://github.com/liyucheng09/Metaphor_Generator/raw/master/CMC/zh_CMC2.txt'
+
+class CMC(datasets.GeneratorBasedBuilder):
+
+    BUILDER_CONFIG_CLASS=datasets.BuilderConfig
+
+    def _info(self):
+
+        feature = {
+            'sent': datasets.Value('string'),
+            'tenor': datasets.Value('string'),
+            'comparator': datasets.Value('string'),
+            'vehicle':datasets.Value('string'),
+        }
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(feature),
+            homepage=_HOMEPAGE,
+            citation=_CITATION
+        )
+    
+    def _split_generators(self, dl_manager):
+        
+        cmc_file = dl_manager.download(_URL)
+
+        return datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={'cmc_file': cmc_file}),
+        # return [
+        #     datasets.SplitGenerator(name='hard', gen_kwargs={'filepath': self.config.data_path}),
+        # ]
+    
+    def _generate_examples(self, cmc_file):
+        with open(cmc_file, encoding='utf-8') as f:
+            count = 0
+            for line in f:
+                if not line:
+                    continue
+                data = json.loads(line)
+                yield count, {
+                    'sent': data['sent'],
+                    'tenor': data['tenor'],
+                    'comparator': data['comparator'],
+                    'vehicle': data['vehicle']
+                }
+                count+=1