Update joud_sample.py

joud_sample.py

@@ -0,0 +1,150 @@
+import csv
+import json
+import os
+
+import datasets
+import gzip
+
+# 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 = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# 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 = ""
+
+# 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 = "https://huggingface.co/datasets/asas-ai/joud_sample/resolve/main/ar_{SUBSET}{split}_{index}.json"
+
+_N_FILES_PER_SPLIT = {
+    'abw': {'train':4 },
+    'mc4': {'train':4 },
+    'wiki':{'train':3 },
+    'cc100': {'train': 4 },
+    'hplt': {'train': 4},
+    'opus': {'train': 6},
+    'tashkeela': {'train': 1},
+    'oscar': {'train': 4},
+}
+
+_SUBSETS = ['abw', 'mc4', 'wiki', 'cc100', 'hplt', 'opus', 'tashkeela', 'oscar' ]
+# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
+
+class JoudConfig(datasets.BuilderConfig):
+    """ Builder config for Joud Dataset. """
+
+    def __init__(self, subset, **kwargs):
+        super(JoudConfig, self).__init__(**kwargs)
+
+        if subset !="all":
+            
+            self.subset = [subset]
+        else:
+            self.subset = _SUBSETS
+            
+class Joud(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+    BUILDER_CONFIGS_CLASS = JoudConfig
+    BUILDER_CONFIGS = [
+        JoudConfig(name=subset,
+        subset=subset,
+        version=datasets.Version("1.1.0", ""),
+        description='') 
+        for subset in _SUBSETS
+    ] + [
+        JoudConfig(
+            name = "all",
+            subset="all",
+            version=datasets.Version("1.1.0", ""),
+            description='')
+    ]
+
+
+    def _info(self):
+        # information about the datasets and feature type of the datasets items.
+        
+        features = datasets.Features(
+            {
+                "text": datasets.Value("string"),
+                "meta": datasets.Value("string"),
+            }
+        )
+        
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,  
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        #split = 'train'    
+        #print("Split")
+        data_urls = {}
+        for split in ['train']: #'validation']:
+            #if self.config.subset = "all":
+            
+            data_urls[split] = [
+                _URLS.format(
+                    SUBSET = subset,
+                    split='validation' if split=='_val' else '',
+                    index = i,
+                )
+                for subset in self.config.subset
+                for i in range(_N_FILES_PER_SPLIT[subset][split])
+            ]
+
+        train_downloaded_files = dl_manager.download(data_urls["train"])
+        #validation_downloaded_files = dl_manager.download(data_urls["validation"])
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": train_downloaded_files}),
+            #datasets.SplitGenerator(
+            #    name=datasets.Split.VALIDATION, gen_kwargs={"filepaths": validation_downloaded_files}
+            #),
+        ]        
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepaths": data_dir,
+                    "split": "train",
+                },
+            ),
+          
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepaths):
+        
+        id_ = 0
+        for filepath in filepaths:
+            with gzip.open(open(filepath,"rb"), "rt", encoding = "utf-8") as f:
+                for row in f:
+                    if row:
+                        
+                        data = json.loads(row)
+                        yield id_, data
+                        id_ +=1