upload hubscripts/paramed_hub.py to hub from bigbio repo

paramed.py

@@ -0,0 +1,197 @@
+# coding=utf-8
+# Copyright 2020 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.
+
+"""
+NEJM is a Chinese-English parallel corpus crawled from the New England Journal of Medicine website.
+English articles are distributed through https://www.nejm.org/ and Chinese articles are distributed through
+http://nejmqianyan.cn/. The corpus contains all article pairs (around 2000 pairs) since 2011.
+The script loads dataset in bigbio schema (using schemas/text-to-text) AND/OR source (default) schema
+"""
+import os  # useful for paths
+from typing import Dict, Iterable, List
+
+import datasets
+
+from .bigbiohub import text2text_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_LANGUAGES = ['English', 'Chinese']
+_PUBMED = False
+_LOCAL = False
+_CITATION = """\
+@article{liu2021paramed,
+  author    = {Liu, Boxiang and Huang, Liang},
+  title     = {ParaMed: a parallel corpus for English–Chinese translation in the biomedical domain},
+  journal   = {BMC Medical Informatics and Decision Making},
+  volume    = {21},
+  year      = {2021},
+  url       = {https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01621-8},
+  doi       = {10.1186/s12911-021-01621-8}
+}
+"""
+_DATASETNAME = "paramed"
+_DISPLAYNAME = "ParaMed"
+
+_DESCRIPTION = """\
+NEJM is a Chinese-English parallel corpus crawled from the New England Journal of Medicine website. 
+English articles are distributed through https://www.nejm.org/ and Chinese articles are distributed through 
+http://nejmqianyan.cn/. The corpus contains all article pairs (around 2000 pairs) since 2011.
+"""
+
+_HOMEPAGE = "https://github.com/boxiangliu/ParaMed"
+
+_LICENSE = 'Creative Commons Attribution 4.0 International'
+
+_URLs = {
+    "source": "https://github.com/boxiangliu/ParaMed/blob/master/data/nejm-open-access.tar.gz?raw=true",
+    "bigbio_t2t": "https://github.com/boxiangliu/ParaMed/blob/master/data/nejm-open-access.tar.gz?raw=true",
+}
+_SUPPORTED_TASKS = [Tasks.TRANSLATION]
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+_DATA_DIR = "./processed_data/open_access/open_access"
+
+
+class ParamedDataset(datasets.GeneratorBasedBuilder):
+    """Write a short docstring documenting what this dataset is"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="paramed_source",
+            version=SOURCE_VERSION,
+            description="Paramed source schema",
+            schema="source",
+            subset_id="paramed",
+        ),
+        BigBioConfig(
+            name="paramed_bigbio_t2t",
+            version=BIGBIO_VERSION,
+            description="Paramed BigBio schema",
+            schema="bigbio_t2t",
+            subset_id="paramed",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "paramed_source"
+
+    def _info(self):
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "document_id": datasets.Value("string"),
+                    "text_1": datasets.Value("string"),
+                    "text_2": datasets.Value("string"),
+                    "text_1_name": datasets.Value("string"),
+                    "text_2_name": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "bigbio_t2t":
+            features = text2text_features
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(
+        self, dl_manager: datasets.DownloadManager
+    ) -> List[datasets.SplitGenerator]:
+
+        my_urls = _URLs[self.config.schema]
+        data_dir = os.path.join(dl_manager.download_and_extract(my_urls), _DATA_DIR)
+        print(data_dir)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "zh_file": os.path.join(data_dir, "nejm.train.zh"),
+                    "en_file": os.path.join(data_dir, "nejm.train.en"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "zh_file": os.path.join(data_dir, "nejm.dev.zh"),
+                    "en_file": os.path.join(data_dir, "nejm.dev.en"),
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "zh_file": os.path.join(data_dir, "nejm.test.zh"),
+                    "en_file": os.path.join(data_dir, "nejm.test.en"),
+                    "split": "test",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, zh_file, en_file, split):
+
+        logger.info("generating examples from = %s", filepath)
+        zh_file = open(zh_file, "r")
+        en_file = open(en_file, "r")
+        zh_file.seek(0)
+        en_file.seek(0)
+        zh_lines = zh_file.readlines()
+        en_lines = en_file.readlines()
+
+        assert len(en_lines) == len(zh_lines), "Line mismatch"
+
+        if self.config.schema == "source":
+            for key, (zh_line, en_line) in enumerate(zip(zh_lines, en_lines)):
+                yield key, {
+                    "document_id": str(key),
+                    "text_1": zh_line,
+                    "text_2": en_line,
+                    "text_1_name": "zh",
+                    "text_2_name": "en",
+                }
+            zh_file.close()
+            en_file.close()
+
+        elif self.config.schema == "bigbio_t2t":
+            uid = 0
+            for key, (zh_line, en_line) in enumerate(zip(zh_lines, en_lines)):
+                uid += 1
+                yield key, {
+                    "id": str(uid),
+                    "document_id": str(key),
+                    "text_1": zh_line,
+                    "text_2": en_line,
+                    "text_1_name": "zh",
+                    "text_2_name": "en",
+                }
+            zh_file.close()
+            en_file.close()