Create testdataset.py

testdataset.py

@@ -0,0 +1,172 @@
+# 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.
+
+"""
+This is inpsired from the mednil implementation: https://huggingface.co/datasets/bigbio/mednli/blob/main/mednli.py
+
+The files comprising this dataset must be on the users local machine in a single directory that is
+passed to `datasets.load_datset` via the `data_dir` kwarg. This loader script will read the archive
+files directly (i.e. the user should not uncompress, untar or unzip any of the files). For example,
+if `data_dir` is `"testdataset"` it should contain the following files:
+testdataset
+├── testdataset.zip
+"""
+
+import json
+import os
+from typing import Dict, List, Tuple
+
+import datasets
+
+# from .bigbiohub import entailment_features
+# from .bigbiohub import BigBioConfig
+# from .bigbiohub import Tasks
+
+
+_LANGUAGES = ["English"]
+_PUBMED = False
+_LOCAL = True
+
+
+_DATASETNAME = "testdataset"
+_DISPLAYNAME = "TESTDATASET"
+
+_DESCRIPTION = """\
+Test Dataset
+"""
+
+
+_HOMEPAGE = "https://physionet.org/content/mednli/1.0.0/"
+
+_LICENSE = "PHYSIONET_LICENSE_1p5"
+
+_URLS = {}
+
+# _SUPPORTED_TASKS = [Tasks.TEXTUAL_ENTAILMENT]
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class MedNLIDataset(datasets.GeneratorBasedBuilder):
+    """MedNLI"""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="mednli_source",
+            version=SOURCE_VERSION,
+            description="MedNLI source schema",
+            schema="source",
+            subset_id="mednli",
+        ),
+        BigBioConfig(
+            name="mednli_bigbio_te",
+            version=BIGBIO_VERSION,
+            description="MedNLI BigBio schema",
+            schema="bigbio_te",
+            subset_id="mednli",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "mednli_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "pairID": datasets.Value("string"),
+                    "gold_label": datasets.Value("string"),
+                    "sentence1": datasets.Value("string"),
+                    "sentence2": datasets.Value("string"),
+                    "sentence1_parse": datasets.Value("string"),
+                    "sentence2_parse": datasets.Value("string"),
+                    "sentence1_binary_parse": datasets.Value("string"),
+                    "sentence2_binary_parse": datasets.Value("string"),
+                }
+            )
+
+        elif self.config.schema == "bigbio_te":
+            features = entailment_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
+        if self.config.data_dir is None:
+            raise ValueError(
+                "This is a local dataset. Please pass the data_dir kwarg to load_dataset."
+            )
+        else:
+            extract_dir = dl_manager.extract(
+                os.path.join(
+                    self.config.data_dir,
+                    "mednli-a-natural-language-inference-dataset-for-the-clinical-domain-1.0.0.zip",
+                )
+            )
+            data_dir = os.path.join(
+                extract_dir,
+                "mednli-a-natural-language-inference-dataset-for-the-clinical-domain-1.0.0",
+            )
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "mli_train_v1.jsonl"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "mli_test_v1.jsonl"),
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "mli_dev_v1.jsonl"),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
+        with open(filepath, "r") as f:
+            if self.config.schema == "source":
+                for line in f:
+                    json_line = json.loads(line)
+                    yield json_line["pairID"], json_line
+
+            elif self.config.schema == "bigbio_te":
+                for line in f:
+                    json_line = json.loads(line)
+                    entailment_example = {
+                        "id": json_line["pairID"],
+                        "premise": json_line["sentence1"],
+                        "hypothesis": json_line["sentence2"],
+                        "label": json_line["gold_label"],
+                    }
+                    yield json_line["pairID"], entailment_example