nateraw commited on
Commit
8a6ee80
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1 Parent(s): 33268ec

🔥remove print statement

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Files changed (1) hide show
  1. wit.py +2 -5
wit.py CHANGED
@@ -4,8 +4,7 @@ import datasets
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  from datasets import Value, Sequence, Features
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- _CITATION = """\
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- @article{srinivasan2021wit,
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  title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
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  author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
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  journal={arXiv preprint arXiv:2103.01913},
@@ -13,8 +12,7 @@ _CITATION = """\
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  }
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  """
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- _DESCRIPTION = """\
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- Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set
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  of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its
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  size enables WIT to be used as a pretraining dataset for multimodal machine learning models.
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  """
@@ -67,7 +65,6 @@ class Wit(datasets.GeneratorBasedBuilder):
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  """Returns SplitGenerators."""
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  urls_to_download = _URLS
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  downloaded_files = dl_manager.download_and_extract(urls_to_download)
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- print(downloaded_files)
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  return [
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  datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_files["train"]}),
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  ]
 
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  from datasets import Value, Sequence, Features
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+ _CITATION = """\\n@article{srinivasan2021wit,
 
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  title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
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  author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
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  journal={arXiv preprint arXiv:2103.01913},
 
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  }
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  """
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+ _DESCRIPTION = """\\nWikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set
 
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  of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its
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  size enables WIT to be used as a pretraining dataset for multimodal machine learning models.
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  """
 
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  """Returns SplitGenerators."""
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  urls_to_download = _URLS
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  downloaded_files = dl_manager.download_and_extract(urls_to_download)
 
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  return [
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  datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_files["train"]}),
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  ]