Create chess_ground-targz.py

chess_ground-targz.py

@@ -0,0 +1,92 @@
+# TODO: Address all TODOs and remove all explanatory comments
+"""chess ground Targz"""
+
+import json
+import os
+
+import datasets
+from pickleshare import Path
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+Dataset for extracting notations from chess-scoresheets.
+"""
+# 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 a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "Creative Commons Attribution 3.0"
+
+# 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)
+_URL = "https://huggingface.co/datasets/Chesscorner/jsonl-chess-dataset/tree/main"
+
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+class ChessGroundTargz(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+    
+    def _info(self):  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "sentence": datasets.Value("string"),
+                    "option1": datasets.Value("string"),
+                    "answer": 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):
+        # 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
+        path = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": os.path.join(path+"/train.jsonl"),
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath):
+
+        idx = 0
+        # open the file and read the lines
+        with open(filepath, encoding="utf-8") as fp:
+            for line in fp:
+                # load json line
+                obj = json.loads(line)
+                yield idx, obj
+                idx += 1