🔥remove print statement
Browse files
wit.py
CHANGED
|
@@ -4,8 +4,7 @@ import datasets
|
|
| 4 |
from datasets import Value, Sequence, Features
|
| 5 |
|
| 6 |
|
| 7 |
-
_CITATION = """\
|
| 8 |
-
@article{srinivasan2021wit,
|
| 9 |
title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
|
| 10 |
author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
|
| 11 |
journal={arXiv preprint arXiv:2103.01913},
|
|
@@ -13,8 +12,7 @@ _CITATION = """\
|
|
| 13 |
}
|
| 14 |
"""
|
| 15 |
|
| 16 |
-
_DESCRIPTION = """\
|
| 17 |
-
Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set
|
| 18 |
of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its
|
| 19 |
size enables WIT to be used as a pretraining dataset for multimodal machine learning models.
|
| 20 |
"""
|
|
@@ -67,7 +65,6 @@ class Wit(datasets.GeneratorBasedBuilder):
|
|
| 67 |
"""Returns SplitGenerators."""
|
| 68 |
urls_to_download = _URLS
|
| 69 |
downloaded_files = dl_manager.download_and_extract(urls_to_download)
|
| 70 |
-
print(downloaded_files)
|
| 71 |
return [
|
| 72 |
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_files["train"]}),
|
| 73 |
]
|
|
|
|
| 4 |
from datasets import Value, Sequence, Features
|
| 5 |
|
| 6 |
|
| 7 |
+
_CITATION = """\\n@article{srinivasan2021wit,
|
|
|
|
| 8 |
title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
|
| 9 |
author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
|
| 10 |
journal={arXiv preprint arXiv:2103.01913},
|
|
|
|
| 12 |
}
|
| 13 |
"""
|
| 14 |
|
| 15 |
+
_DESCRIPTION = """\\nWikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set
|
|
|
|
| 16 |
of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its
|
| 17 |
size enables WIT to be used as a pretraining dataset for multimodal machine learning models.
|
| 18 |
"""
|
|
|
|
| 65 |
"""Returns SplitGenerators."""
|
| 66 |
urls_to_download = _URLS
|
| 67 |
downloaded_files = dl_manager.download_and_extract(urls_to_download)
|
|
|
|
| 68 |
return [
|
| 69 |
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_files["train"]}),
|
| 70 |
]
|