Datasets:

KevinZ
/

psycholinguistic_eval

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CPRAG/dataset_infos.json ADDED Viewed

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CPRAG/test.csv ADDED Viewed

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Ettinger.py ADDED Viewed

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+# 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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+import datasets
+import pandas as pd
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {A great new dataset},
+author={huggingface, Inc.
+},
+year={2020}
+}
+"""
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_PATHS = {
+    "CPRAG": "CPRAG/test.csv",
+    "ROLE": "ROLE/test.csv",
+    "NEG-NAT": "NEG-NAT/test.csv",
+    "NEG-SIMP": "NEG-SIMP/test.csv",
+}
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class NewDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+    VERSION = datasets.Version("1.0.0")
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="CPRAG", version=VERSION, description="[CPRAG description]"),
+        datasets.BuilderConfig(name="ROLE", version=VERSION, description="[ROLE description]"),
+        datasets.BuilderConfig(name="NEG-NAT", version=VERSION, description="[NEG-NAT description]"),
+        datasets.BuilderConfig(name="NEG-SIMP", version=VERSION, description="[NEG-SIMP description]"),
+    ]
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        if self.config.name == "CPRAG":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "context_s1": datasets.Value("string"),
+                    "context_s2": datasets.Value("string"),
+                    "expected": datasets.Value("string"),
+                    "within_category": datasets.Value("string"),
+                    "between_category": datasets.Value("string"),
+                }
+            )
+        elif self.config.name == "ROLE":
+            features = datasets.Features(
+                {
+                    "context": datasets.Value("string"),
+                    "expected": datasets.Value("string"),
+                }
+            )
+        elif self.config.name == "NEG-NAT":
+            features = datasets.Features(
+                {
+                    "context_aff": datasets.Value("string"),
+                    "context_neg": datasets.Value("string"),
+                    "target_aff": datasets.Value("string"),
+                    "target_neg": datasets.Value("string"),
+                }
+            )
+        elif self.config.name == "NEG-SIMP":
+            features = datasets.Features(
+                {
+                    "context_aff": datasets.Value("string"),
+                    "context_neg": datasets.Value("string"),
+                    "target_aff": datasets.Value("string"),
+                    "target_neg": datasets.Value("string"),
+                }
+            )
+        else:
+            raise NotImplementedError
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": _PATHS[self.config.name],
+                    "split": "test"
+                },
+            ),
+        ]
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        df = pd.read_csv(filepath)
+        for index, row in df.iterrows():
+            if self.config.name == "NEG-NAT":
+                features = datasets.Features(
+                    {
+                        "context_aff": datasets.Value("string"),
+                        "context_neg": datasets.Value("string"),
+                        "target_aff": datasets.Value("string"),
+                        "target_neg": datasets.Value("string"),
+                    }
+                )
+            elif self.config.name == "NEG-SIMP":
+                features = datasets.Features(
+                    {
+                        "context_aff": datasets.Value("string"),
+                        "context_neg": datasets.Value("string"),
+                        "target_aff": datasets.Value("string"),
+                        "target_neg": datasets.Value("string"),
+                    }
+                )
+            if self.config.name == "CPRAG":
+                # Yields examples as (key, example) tuples
+                yield index, {
+                    "context_s1": row["context_s1"],
+                    "context_s2": row["context_s2"],
+                    "expected": row["expected"],
+                    "within_category": row["within_category"],
+                    "between_category": row["between_category"],
+                }
+            elif self.config.name == "ROLE":
+                yield index, {
+                    "context": row["context"],
+                    "expected": row["expected"],
+                }
+            elif self.config.name == "NEG-NAT":
+                yield index, {
+                    "context_aff": row["context_aff"],
+                    "context_neg": row["context_neg"],
+                    "target_aff": row["target_aff"],
+                    "target_neg": row["target_neg"],
+                }
+            elif self.config.name == "NEG-SIMP":
+                yield index, {
+                    "context_aff": row["context_aff"],
+                    "context_neg": row["context_neg"],
+                    "target_aff": row["target_aff"],
+                    "target_neg": row["target_neg"],
+                }
+            else:
+                raise NotImplementedError

NEG-NAT/dataset_infos.json ADDED Viewed

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NEG-SIMP/dataset_infos.json ADDED Viewed

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README.md CHANGED Viewed

@@ -1,3 +1,4 @@
 ---
 license: mit
 ---

 ---
 license: mit
 ---
+Copyright (c) 2020 Allyson Ettinger

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