first update

README.md

@@ -0,0 +1,6 @@
+---
+task_categories:
+- text-classification
+---
+
+This dataset is a sample used to learn how to share dataset to the Hub.

share_test.py

@@ -0,0 +1,171 @@
+"""该脚本是官网下载的模板所改写的"""
+
+
+# import csv
+# import json
+import os
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+import pandas as pd
+
+_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 dataset is a random sample used to learn how to share dataset in the Hub.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "www.huggingface.co/lijianbin"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "123"
+
+# 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 = {
+#     "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
+#     "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
+# }
+_URLS = 'https://huggingface.co/datasets/lijianbin/share_test/'
+
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class ShareTest(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="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
+    #     datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
+    # ]
+
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        features = datasets.Features(
+            {
+                "label": datasets.Value("float"),
+                "x1": datasets.Value("float"),
+                "x2": datasets.Value("float"),
+                "x3": datasets.Value("float"),
+                "x4": datasets.Value("float"),
+                "x5": datasets.Value("float"),
+                "x6": datasets.Value("float"),
+                "x7": datasets.Value("float"),
+                "x8": datasets.Value("float"),
+                "x9": datasets.Value("float"),
+                "x10": datasets.Value("float")
+                # 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):
+        # 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
+        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={
+                    "filepath": os.path.join(data_dir, "data.xlsx"),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "data.xlsx"),
+                    "split": "validation",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(data_dir, "data.xlsx"),
+                    "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.
+        # with open(filepath, encoding="utf-8") as f:
+        #     for key, row in enumerate(f):
+        #         data = json.loads(row)
+        #         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"],
+        #             }
+        df = pd.read_excel(filepath)
+        train_df = df.iloc[:700, :]
+        validation_df = df.iloc[700:900, :]
+        test_df = df.iloc[900:, :]
+        if split == 'train':
+            iteration = train_df
+        elif split == 'validation':
+            iteration = validation_df
+        else:
+            iteration = test_df
+        for key in range(iteration.index.tolist()):
+            yield key, iteration.loc[key, :]