Added custom data loader and added arxiv ids to data

arxivDIGESTables.py

@@ -0,0 +1,198 @@
+# 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 csv
+import json
+import os
+from typing import List, Dict
+
+import datasets
+
+
+# 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 = """\
+ArxivDIGESTables: A dataset of tables from ArXiv papers along with captions, in-text references and associated full-texts."""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://github.com/bnewm0609/arxivDIGESTables"
+
+# 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)
+_URLS = {
+    "high_quality": "/Users/blnewman/Documents/research/projects/paper_comparison/arxivDIGESTables_hf_data/tables.jsonl", # "https://huggingface.co/datasets/blnewman/arxivDIGESTables/resolve/main/tables.jsonl",
+    "medium_quality": "/Users/blnewman/Documents/research/projects/paper_comparison/arxivDIGESTables_hf_data/tables_medium_quality.jsonl", # "https://huggingface.co/datasets/blnewman/arxivDIGESTables/resolve/main/tables_medium_quality.jsonl",
+}
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class ArxivDIGESTables(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # 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="high_quality", version=VERSION, description="High quality, manually corrected tables."),
+        datasets.BuilderConfig(name="medium_quality", version=VERSION, description="Medium quality tables."),
+    ]
+
+    DEFAULT_CONFIG_NAME = "high_quality"  # 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
+        if self.config.name == "high_quality":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "tabid": datasets.Value("string"),
+                    "table": datasets.Value("string"),
+                    "row_bib_map": [{
+                        "bib_hash_or_arxiv_id": datasets.Value("string"),
+                        "row": datasets.Value("int64"),
+                        "corpus_id": datasets.Value("int64"),
+                        "type": datasets.Value("string"),
+                        "title": datasets.Value("string"),
+                        "abstract": datasets.Value("string"),
+                    }],
+                    "caption": datasets.Value("string"),
+                    "in_text_ref": [{
+                        "section": datasets.Value("string"),
+                        "sec_number": datasets.Value("string"),
+                        "sec_type": datasets.Value("string"),
+                        "content_type": datasets.Value("string"),
+                        "text": datasets.Value("string"),
+                        "cite_spans": [{
+                            "start": datasets.Value("int64"),
+                            "end": datasets.Value("int64"),
+                            "text": datasets.Value("string"),
+                            "ref_id": datasets.Value("string"),
+                        }],
+                        "ref_spans": [{
+                            "start": datasets.Value("int64"),
+                            "end": datasets.Value("int64"),
+                            "text": datasets.Value("string"),
+                            "ref_id": datasets.Value("string"),
+                        }],
+                    }],
+                    "arxiv_id": datasets.Value("string"),
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
+        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
+            features = datasets.Features(
+                {
+                    "tabid": datasets.Value("string"),
+                    "table": datasets.Value("string"),
+                    "row_bib_map": [{
+                        "bib_hash_or_arxiv_id": datasets.Value("string"),
+                        "row": datasets.Value("int64"),
+                        "corpus_id": datasets.Value("int64"),
+                        "type": datasets.Value("string"),
+                        "title": datasets.Value("string"),
+                        "abstract": datasets.Value("string"),
+                    }],
+                    "arxiv_id": datasets.Value("string"),
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
+        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):
+        print("hi", flush=True)
+        # 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
+        urls = _URLS[self.config.name]
+        # data_path = dl_manager.download_and_extract(urls)
+        print(urls)
+        data_path = urls
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_path,
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    # 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.
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                data["table"] = json.dumps(data["table"])
+                # print(data.k)
+                yield key, data
+                # if self.config.name == "first_domain":
+                #     # Yields examples as (key, example) tuples
+                #     yield key, {
+                #         "sentence": data["sentence"],
+                #         "option1": data["option1"],
+                #         "answer": "" if split == "test" else data["answer"],
+                #     }
+                # else:
+                #     yield key, {
+                #         "sentence": data["sentence"],
+                #         "option2": data["option2"],
+                #         "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
+                #     }

tables.jsonl

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+oid sha256:90a80e645e3050041e635c188ac64c666a6debc533fb8bcc6b041dc3045f18d1
+size 26161029

tables_medium_quality.jsonl

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+size 253533544