Upload my_paraphrase.py with huggingface_hub

my_paraphrase.py

@@ -0,0 +1,200 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+import pandas as pd
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Licenses, Tasks
+
+_CITATION = """\
+@article{htay2022deep,
+  title={Deep Siamese Neural Network Vs Random Forest for Myanmar Language Paraphrase Classification},
+  author={Htay, Myint Myint and Thu, Ye Kyaw and Thant, Hnin Aye and Supnithi, Thepchai},
+  journal={Journal of Intelligent Informatics and Smart Technology},
+  year={2022}
+}
+"""
+
+_DATASETNAME = "my_paraphrase"
+
+_DESCRIPTION = """\
+The myParaphrase corpus is intended for the task of assessing whether pairs of Burmese sentences exhibit similar meanings \
+or are paraphrases. It encompasses 40461 pairs for training, along with 1000 pairs for an open test and an additional 1000 pairs \
+for a closed test. If a pair of sentences in Burmese is considered a paraphrase, it is labeled with "1"; if not, they receive a label of "0."
+"""
+
+_HOMEPAGE = "https://github.com/ye-kyaw-thu/myParaphrase"
+
+_LANGUAGES = ["mya"]
+
+_LICENSE = Licenses.CC_BY_NC_SA_4_0.value
+_LOCAL = False
+
+_URLS = {
+    _DATASETNAME: [
+        "https://github.com/ye-kyaw-thu/myParaphrase/raw/main/corpus/ver1.0/csv-qqp/train.csv",
+        "https://github.com/ye-kyaw-thu/myParaphrase/raw/main/corpus/ver1.0/csv-qqp/open-test.final.manual.csv",
+        "https://github.com/ye-kyaw-thu/myParaphrase/raw/main/corpus/ver1.0/csv-qqp/closed-test.csv",
+    ],
+}
+
+_SUPPORTED_TASKS = [Tasks.PARAPHRASING]
+_SOURCE_VERSION = "1.0.0"
+_SEACROWD_VERSION = "2024.06.20"
+_TAGS = [0, 1]
+
+
+class MyParaphraseDataset(datasets.GeneratorBasedBuilder):
+    """The "myParaphrase" corpus is a Burmese dataset used for paraphrase identification. \
+    It includes 40,461 training pairs and 2,000 test pairs. Pairs are labeled "1" for paraphrases and "0" otherwise."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+    SEACROWD_SCHEMA_NAME = "t2t"
+
+    BUILDER_CONFIGS = [
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_source",  # source
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}_paraphrase",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}",  # schema
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=f"{_DATASETNAME}_paraphrase",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_non_paraphrase_source",  # source
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}_non_paraphrase",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_non_paraphrase_seacrowd_{SEACROWD_SCHEMA_NAME}",  # schema
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=f"{_DATASETNAME}_non_paraphrase",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_all_source",  # source
+            version=SOURCE_VERSION,
+            description=f"{_DATASETNAME} source schema",
+            schema="source",
+            subset_id=f"{_DATASETNAME}_all",
+        ),
+        SEACrowdConfig(
+            name=f"{_DATASETNAME}_all_seacrowd_{SEACROWD_SCHEMA_NAME}",  # schema
+            version=SEACROWD_VERSION,
+            description=f"{_DATASETNAME} SEACrowd schema",
+            schema=f"seacrowd_{SEACROWD_SCHEMA_NAME}",
+            subset_id=f"{_DATASETNAME}_all",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = f"{_DATASETNAME}_seacrowd_{SEACROWD_SCHEMA_NAME}"
+
+    def _info(self) -> datasets.DatasetInfo:
+        if self.config.schema.endswith("_source"):
+            features = datasets.Features({"id": datasets.Value("int32"), "paraphrase1": datasets.Value("string"), "paraphrase2": datasets.Value("string"), "is_paraphrase": datasets.Value("int32")})
+
+        elif self.config.schema.endswith(self.SEACROWD_SCHEMA_NAME):
+            features = schemas.text2text_features
+        
+        else:
+            raise ValueError
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        urls = _URLS[_DATASETNAME]
+        train = dl_manager.download(urls[0])
+        open_test = dl_manager.download(urls[1])
+        closed_test = dl_manager.download(urls[2])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # Whatever you put in gen_kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": train,
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": closed_test,
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": open_test,
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+
+        columns = ["id", "paraphrase1", "paraphrase2", "is_paraphrase"]
+        dataset = pd.read_csv(filepath, header=None)
+        dataset.columns = columns
+        dataset = dataset.dropna()
+
+        dataset["is_paraphrase"] = dataset["is_paraphrase"].astype(int)
+
+        if self.config.schema in [
+            "paraphrase_source",
+            "non_paraphrase_source",
+            "all_source",
+            # "source"
+        ]:
+            for i, row in dataset.iterrows():
+                yield i, {"id": i, "paraphrase1": row["paraphrase1"], "paraphrase2": row["paraphrase2"], "is_paraphrase": row["is_paraphrase"]}
+
+        elif self.config.schema == f"seacrowd_paraphrase_{self.SEACROWD_SCHEMA_NAME}":
+            for i, row in dataset[dataset["is_paraphrase"] == 1].iterrows():
+                yield i, {"id": i, "text_1": row["paraphrase1"], "text_2": row["paraphrase2"], "text_1_name": "anchor_text", "text_2_name": "paraphrased_text"}
+
+        elif self.config.schema == f"seacrowd_non_paraphrase_{self.SEACROWD_SCHEMA_NAME}":
+            for i, row in dataset[dataset["is_paraphrase"] == 0].iterrows():
+                yield i, {"id": i, "text_1": row["paraphrase1"], "text_2": row["paraphrase2"], "text_1_name": "anchor_text", "text_2_name": "non_paraphrased_text"}
+
+        elif self.config.schema == f"seacrowd_all_{self.SEACROWD_SCHEMA_NAME}":
+            for i, row in dataset.iterrows():
+                yield i, {"id": i, "text_1": row["paraphrase1"], "text_2": row["paraphrase2"], "text_1_name": "anchor_text", "text_2_name": "paraphrased_text" if row["is_paraphrase"] else "non_paraphrased_text"}
+
+        else:
+            raise ValueError