loading script created

data/dataset_infos.json

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fewshot_pretraining_loading_script.py

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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.
+"""This loads the fewshot-pretraining dataset."""
+
+import json
+import os
+import pandas as pd
+
+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}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+The Fewshot Table dataset consists of tables that naturally occur on the web, that are formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. The dataset consists of approximately 413K tables that are extracted from the WDC Web Table Corpora 2015, which is released under the Apache-2.0 license. The WDC Web Table Corpora "contains vast amounts of HTML tables. [...] The Web Data Commons project extracts relational Web tables from the Common Crawl, the largest and most up-to-date Web corpus that is currently available to the public."
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+_LICENSE = "Apache 2.0"
+
+# 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 = {
+    "data_1": "https://huggingface.co/datasets/JeremyAlain/fewshot-ptretraining/data/1",
+    "data_2": "https://huggingface.co/datasets/JeremyAlain/fewshot-ptretraining/data/2",
+    "data_3": "https://huggingface.co/datasets/JeremyAlain/fewshot-ptretraining/data/3",
+
+}
+
+
+class FewshotPretraining(datasets.GeneratorBasedBuilder):
+    """The Fewshot Table dataset consists of tables that naturally occur on the web, that are formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. The dataset consists of approximately 413K tables that are extracted from the WDC Web Table Corpora 2015, which is released under the Apache-2.0 license. The WDC Web Table Corpora "contains vast amounts of HTML tables. [...] The Web Data Commons project extracts relational Web tables from the Common Crawl, the largest and most up-to-date Web corpus that is currently available to the public."
+"""
+
+    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.
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', '1')
+    # data = datasets.load_dataset('my_dataset', '2')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="data_1", version=VERSION, description="This part of my dataset covers data_1"),
+        datasets.BuilderConfig(name="data_2", version=VERSION, description="This part of my dataset covers data_2"),
+        datasets.BuilderConfig(name="data_3", version=VERSION, description="This part of my dataset covers data_3"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "data_1"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        features = datasets.Features(
+            {
+
+                "task": datasets.Value("string"),
+                "input": datasets.Value("string"),
+                "output": datasets.Value("string"),
+                "options": datasets.Sequence([datasets.Value("string")]),
+                "pageTitle": datasets.Value("string"),
+                "outputColName": datasets.Value("string"),
+                "url": datasets.Value("string"),
+                "wdcFile": 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, 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
+            # TODO ACTIVATE IF WE HAVE HOMEPAGE 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
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        return datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "folder_path": data_dir,
+                    "split": "train",
+                },
+            )
+
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, folder_path, split):
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        for filepath in os.listdir(folder_path):
+            with open(filepath, encoding="utf-8") as f:
+                data = pd.read_json(filepath, orient="records", lines=True)
+                for i in range(data.shape[0]):
+                    row = data.iloc[i]
+                    # Yields examples as (key, example) tuples
+                    key = row["task"] + "_i"
+                    yield key, {
+                        "task": data["task"],
+                        "input": data["input"],
+                        "output": data["output"],
+                        "options": data["options"],
+                        "pageTitle": data["pageTitle"],
+                        "outputColName": data["outputColName"],
+                        "url": data["url"],
+                        "wdcFile": data["wdcFile"],
+                    }