Create load_dataset.py

load_dataset.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.
+"""
+    Exercise 2: Part 2 - Extractive QA 30 points ,
+    storing a dataset with HuggingFace
+"""
+
+import csv
+import json
+import os
+
+import datasets
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{air21_grp13:tokenized_results,
+title = {Adv. Information Retrieval - Exercise , Part 2: Storing Results with HuggingFace},
+author={Alexander Genser, Lena Jiricka, Samuel Keller
+},
+year={2021}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset are results from extractive QA, using our top-1 re-ranking results from our implementation of part 1
+and the fira gold label 
+"""
+
+_HOMEPAGE = "https://github.com/tuwien-information-retrieval/air-2021-group_13"
+
+_LICENSE = "MIT"
+
+# 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)
+_URLs = {
+    'first_domain': "https://huggingface.co/datasets/huggingFaceUser02/air21_grp13_tokenized_results.zip",
+    'second_domain': "https://huggingface.co/datasets/huggingFaceUser02/air21_grp13_tokenized_results.zip",
+}
+
+
+class AIR21Grp13TokenizedResults(datasets.GeneratorBasedBuilder):
+    """
+results from the top-1 re-ranking results from implementation of part 1 of Exercise 2, AIR 21.
+These are compared with the FiRA gold label results, using the BERT Transformer
+'bert-large-uncased-whole-word-masking-finetuned-squad'
+    """
+
+    VERSION = datasets.Version("0.1.0")
+
+    # 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="inference_results", version=VERSION,
+                               description="This part of my dataset covers the results of the inference")
+    ]
+
+    DEFAULT_CONFIG_NAME = "inference_results"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        features = datasets.Features(
+                {
+                    "doc_id": datasets.Value("long"),
+                    "query_id": datasets.Value("long"),
+                    "relevance": datasets.Value("float"),
+                    "query_sentence": 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]
+        data_dir = dl_manager.download_and_extract(my_urls)
+        return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    # These kwargs will be passed to _generate_examples
+                    gen_kwargs={
+                        "filepath": os.path.join(data_dir, "top1_bert_large_uncased_answers_01.tsv"),
+                        "split": "dev",
+                    },
+                ),
+            ]
+
+    def _generate_examples(
+            self, filepath, 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.
+
+        with open(filepath, encoding="utf-8") as f:
+            # logger.info("Reading instances from lines in file at: %s", file_path)
+            for line_num, line in enumerate(f):
+                line = line.strip("\n")
+
+                if not line:
+                    continue
+
+                line_parts = line.split('\t')
+
+                query_id, doc_id, relevance, answer = line_parts
+
+                yield {
+                    "query_id": query_id,
+                    "doc_id": doc_id,
+                    "relevance": relevance,
+                    "answer": answer
+                }
+