upload hubscripts/bio_simlex_hub.py to hub from bigbio repo

bio_simlex.py

@@ -0,0 +1,163 @@
+# 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.
+
+"""
+Bio-SimLex enables intrinsic evaluation of word representations. This evaluation
+can serve as a predictor of performance on various downstream tasks in the
+biomedical domain. The results on Bio-SimLex using standard word representation
+models highlight the importance of developing dedicated evaluation resources for
+NLP in biomedicine for particular word classes (e.g. verbs).
+"""
+
+from typing import Dict, List, Tuple
+
+import datasets
+
+from .bigbiohub import pairs_features
+from .bigbiohub import BigBioConfig
+from .bigbiohub import Tasks
+
+_LANGUAGES = ['English']
+_PUBMED = True
+_LOCAL = False
+_CITATION = """\
+@article{article,
+  title        = {
+    Bio-SimVerb and Bio-SimLex: Wide-coverage evaluation sets of word
+    similarity in biomedicine
+  },
+  author       = {Chiu, Billy and Pyysalo, Sampo and Vulić, Ivan and Korhonen, Anna},
+  year         = 2018,
+  month        = {02},
+  journal      = {BMC Bioinformatics},
+  volume       = 19,
+  pages        = {},
+  doi          = {10.1186/s12859-018-2039-z}
+}
+"""
+
+_DATASETNAME = "bio_simlex"
+_DISPLAYNAME = "Bio-SimLex"
+
+_DESCRIPTION = """\
+Bio-SimLex enables intrinsic evaluation of word representations. This evaluation \
+can serve as a predictor of performance on various downstream tasks in the \
+biomedical domain. The results on Bio-SimLex using standard word representation \
+models highlight the importance of developing dedicated evaluation resources for \
+NLP in biomedicine for particular word classes (e.g. verbs).
+"""
+
+_HOMEPAGE = "https://github.com/cambridgeltl/bio-simverb"
+
+_LICENSE = 'License information unavailable'
+
+_URLS = {
+    _DATASETNAME: "https://github.com/cambridgeltl/bio-simverb/blob/master/wvlib/word-similarities/\
+bio-simlex/Bio-SimLex.txt?raw=true"
+}
+
+_SUPPORTED_TASKS = [Tasks.SEMANTIC_SIMILARITY]
+
+_SOURCE_VERSION = "1.0.0"
+_BIGBIO_VERSION = "1.0.0"
+
+
+class BioSimlexDataset(datasets.GeneratorBasedBuilder):
+    """
+    Bio-SimLex enables intrinsic evaluation of word representations. Config schema
+    as source gives score between 0-10 for pairs of words. The source schema casts
+    labels as `float`, but the bigbio schema casts them as `str`.
+    """
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)
+
+    BUILDER_CONFIGS = [
+        BigBioConfig(
+            name="bio_simlex_source",
+            version=SOURCE_VERSION,
+            description="bio_simlex source schema",
+            schema="source",
+            subset_id="bio_simlex",
+        ),
+        BigBioConfig(
+            name="bio_simlex_bigbio_pairs",
+            version=BIGBIO_VERSION,
+            description="bio_simlex BigBio schema",
+            schema="bigbio_pairs",
+            subset_id="bio_simlex",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "bio_simlex_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "text_1": datasets.Value("string"),
+                    "text_2": datasets.Value("string"),
+                    "score": datasets.Value("float32"),
+                }
+            )
+
+        elif self.config.schema == "bigbio_pairs":
+            features = pairs_features
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=str(_LICENSE),
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:
+        """Returns SplitGenerators."""
+
+        url = _URLS[_DATASETNAME]
+        data_dir = dl_manager.download_and_extract(url)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split: str) -> Tuple[int, Dict]:
+        """Yields examples as (key, example) tuples."""
+        with open(filepath, "r", encoding="utf-8") as f:
+            for id_, line in enumerate(f):
+                word1, word2, score = line.split("\t")
+                if self.config.schema == "source":
+                    yield id_, {
+                        "text_1": word1,
+                        "text_2": word2,
+                        "score": float(score),
+                    }
+
+                elif self.config.schema == "bigbio_pairs":
+                    yield id_, {
+                        "id": str(id_),
+                        "document_id": str(id_),
+                        "text_1": word1,
+                        "text_2": word2,
+                        "label": str(score.strip()),
+                    }