Create wit-dataset.py

wit-dataset.py

@@ -0,0 +1,212 @@
+import datasets
+
+logger = datasets.logging.get_logger(__name__)
+_DESCRIPTION = """\
+Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. 
+WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. 
+Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models.
+"""
+_CITATION = """
+@article{srinivasan2021wit,
+  title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
+  author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
+  journal={arXiv preprint arXiv:2103.01913},
+  year={2021}
+}
+"""
+_URL = "https://github.com/google-research-datasets/wit"
+_DATA_URL = "https://huggingface.co/datasets/keshan/wit-dataset/resolve/7e65a989e0d2e48c33b86309c37e9eadfc063b9f/data/{language}.tar.gz"
+_LANGUAGES = [
+              'ms',
+              'eu',
+              'si',
+              'Prakrit',
+              'ko',
+              'nv',
+              'id',
+              'tg',
+              'mn',
+              'fa',
+              'bg',
+              'ia',
+              'ca',
+              'jv',
+              'vi',
+              'ja',
+              'bs',
+              'te',
+              'war',
+              'hy',
+              'sv',
+              'az',
+              'lah',
+              'ht',
+              'sl',
+              'pt',
+              'an',
+              'br',
+              'nn',
+              'ceb',
+              'ce',
+              'qu',
+              'gl',
+              'fy',
+              'vec',
+              'zh',
+              'iw',
+              'vo',
+              'xmf',
+              'nds',
+              'bar',
+              'ba',
+              'sr-Latn',
+              'hsb',
+              'yue',
+              'arz',
+              'es',
+              'bn',
+              'de',
+              'mk',
+              'pa',
+              'zh-TW',
+              'io',
+              'lb',
+              'azb',
+              'ga',
+              'cs',
+              'fi',
+              'cv',
+              'sr',
+              'lv',
+              'my',
+              'mg',
+              'hu',
+              'it',
+              'kk',
+              'be',
+              'sq',
+              'ru',
+              'ar',
+              'cy',
+              'hr',
+              'be-tarask',
+              'is',
+              'tt',
+              'mr',
+              'ro',
+              'en',
+              'fil',
+              'uz',
+              'af',
+              'et',
+              'fr',
+              'no',
+              'ckb',
+              'nan',
+              'sw',
+              'la',
+              'lmo',
+              'th',
+              'ta',
+              'ast',
+              'eo',
+              'tr',
+              'uk',
+              'ur',
+              'ne',
+              'kn',
+              'da',
+              'nl',
+              'ka',
+              'pl',
+              'el',
+              'sco',
+              'hi',
+              'sk',
+              'oc',
+              'lt',
+              'ml'
+              ]
+
+class WITConfig(datasets.BuilderConfig):
+    """BuilderConfig for WIT."""
+    def __init__(self, *args, languages, **kwargs):
+        """BuilderConfig for WIT.
+        Args:
+            languages (:obj:`List[str]`): list of languages to load
+            **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(
+            *args,
+            name="+".join(languages),
+            **kwargs,
+        )
+        self.languages = languages
+
+class WIT(datasets.GeneratorBasedBuilder):
+    """WIT, WIT to be used as a pretraining dataset for multimodal machine learning models."""
+    BUILDER_CONFIGS = [WITConfig(languages=[lang]) for lang in _LANGUAGES]
+    BUILDER_CONFIG_CLASS = WITConfig
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "language": datasets.Value("string"),
+                    "page_url": datasets.Value("string"),
+                    "image_url": datasets.Value("string"),
+                    "page_title": datasets.Value("string"),
+                    "section_title": datasets.Value("string"),
+                    "hierarchical_section_title": datasets.Value("string"),
+                    "caption_reference_description": datasets.Value("string"),
+                    "caption_attribution_description": datasets.Value("string"),
+                    "caption_alt_text_description": datasets.Value("string"),
+                    "mime_type": datasets.Value("string"),
+                    "original_height": datasets.Value("int8"),
+                    "original_width": datasets.Value("int8"),
+                    "is_main_image": datasets.Value("bool"),
+                    "attribution_passes_lang_id": datasets.Value("string"),
+                    "page_changed_recently": datasets.Value("string"),
+                    "context_page_description": datasets.Value("string"),
+                    "context_section_description": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_URL,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        abs_path_to_data = dl_manager.download_and_extract(
+            _DATA_URL.format(language=self.config.name)
+            )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": abs_path_to_data,
+                },
+            ),
+        ]
+    
+    def _generate_examples(self, filepath):
+      data_fields = list(self._info().features.keys())
+      path_idx = data_fields.index("image_url")
+      # ToDO: Remove after debugging..
+      print(path_to_data)
+      with open(path_to_data, encoding="utf-8") as f:
+          lines = f.readlines()
+          headline = line[0]
+
+          column_names = headline.strip().split('\t')
+          assert (
+                column_names == data_fields
+            ), f"The file should have {data_fields} as column names, but has {column_names}"
+
+          for id_, line in enumerate(lines[1:]):
+            field_values = line.strip().split("\t")
+            # if data is incomplete, fill with empty values
+            if len(field_values) < len(data_fields):
+                field_values += (len(data_fields) - len(field_values)) * ["''"]
+
+            yield id_, {key: value for key, value in zip(data_fields, field_values)}