Initial commit to test in other repository

test.py

@@ -0,0 +1,6 @@
+from datasets import load_dataset
+qrels = load_dataset('trec_cast_2019_multi_turn.py', 'qrels')
+qrels.items()
+
+topics = load_dataset('trec_cast_2019_multi_turn.py', 'topics')
+topics.items()

trec_cast_2019_multi_turn.py

@@ -0,0 +1,154 @@
+# 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.
+import json
+from collections import defaultdict
+
+import datasets
+import csv
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+
+"""
+
+_HOMEPAGE = "http://www.treccast.ai"
+
+_LICENSE = ""
+
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URL = "https://huggingface.co/datasets/uva-irlab/trec-cast-2019-multi-turn/resolve/main/"
+_URLs = {
+    'topics': _URL+"cast2019_test_annotated.tsv",
+    'qrels': _URL+"2019qrels.txt",
+}
+
+
+class TrecCast2019MultiTurn(datasets.GeneratorBasedBuilder):
+    """
+    Voskarides et al. have preprocessed CANARD in different ways depending on their experiment.
+    """
+
+    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="qrels", version=VERSION, description=""),
+        datasets.BuilderConfig(name="topics", version=VERSION, description=""),
+    ]
+
+    # It's not mandatory to have a default configuration. Just use one if it make sense.
+    DEFAULT_CONFIG_NAME = None
+
+    def _info(self):
+        # This is the name of the configuration selected in BUILDER_CONFIGS above
+        if self.config.name == "topics":
+            features = datasets.Features({
+                "qid": datasets.Value("string"),
+                "history": datasets.features.Sequence(feature=datasets.Value('string')),
+                "query": datasets.Value("string"),
+            })
+        elif self.config.name == "qrels":
+            features = datasets.Features({
+                "qid": datasets.Value("string"),
+                "qrels": datasets.features.Sequence(feature=datasets.Features({
+                    'docno': datasets.Value("string"),
+                    'rank': datasets.Value("string"),
+                })),
+            })
+        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,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # 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):
+        """Returns SplitGenerators."""
+        # 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
+        my_urls = _URLs[self.config.name]
+        downloaded_files = dl_manager.download_and_extract(my_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={  # These kwargs will be passed to _generate_examples
+                    "file": downloaded_files,
+                    "split": self.config.name
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self, file, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    ):
+        """ Yields examples as (key, example) tuples. """
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+
+        if split == 'qrels':
+            qrels_file = csv.reader(open(file), delimiter=" ")
+            qrels = defaultdict(list)
+            for row in qrels_file:
+                qid = row[0]
+                docno = row[2]
+                rank = row[3]
+                qrels[qid].append({'rank': rank, 'docno': docno})
+
+            for qid in qrels.keys():
+                yield qid, {'qid': qid, 'qrels': qrels[qid]}
+
+        if split == 'topics':
+            topics_file = csv.reader(open(file), delimiter="\t")
+            topics = defaultdict(list)
+            for row in topics_file:
+                qid, query = row
+                conversation_id, question_number = qid.split('_')
+                topics[conversation_id].append(query)
+
+            for conversation_id in topics.keys():
+                queries = topics[conversation_id]  # type: list
+                for idx in range(len(queries)):
+                    query = queries[idx]
+                    qid = f"{conversation_id}_{str(idx+1)}"
+                    yield qid, ({'query': query, 'history': queries[:idx], 'qid': qid})