Create readingbank.py

readingbank.py

@@ -0,0 +1,149 @@
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""ReadingBank is a benchmark dataset for reading order detection built with weak supervision from WORD documents, which contains 500K document images with a wide range of document types as well as the corresponding reading order information."""
+
+
+from pathlib import Path
+import pandas as pd
+import datasets
+
+
+_CITATION = """\
+@misc{wang2021layoutreader,
+      title={LayoutReader: Pre-training of Text and Layout for Reading Order Detection}, 
+      author={Zilong Wang and Yiheng Xu and Lei Cui and Jingbo Shang and Furu Wei},
+      year={2021},
+      eprint={2108.11591},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DESCRIPTION = """\
+ReadingBank is a benchmark dataset for reading order detection built with weak supervision from WORD documents, which contains 500K document images with a wide range of document types as well as the corresponding reading order information.
+"""
+
+_HOMEPAGE = "https://github.com/doc-analysis/ReadingBank"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+_URLS = {
+    "dataset": "https://layoutlm.blob.core.windows.net/readingbank/dataset/ReadingBank.zip",
+}
+
+def parse_files(files):
+    layout_text = {}
+
+    for i in [1,2,3,4,6,7]:
+        layout_text[f'm{i}'] = {}
+
+    for file in files:
+        stem = file.stem
+        shard = stem.split('-')[-1]
+        if 'text' in stem:
+            layout_text[shard]['text']=file
+        elif 'layout' in stem:
+            layout_text[shard]['layout']=file
+            
+    return layout_text
+
+def get_dataframe(files,split):
+    df_list = []
+    for shard in files.keys():
+        df_list.append(pd.read_json(files[shard][split],lines=True))
+    df = pd.concat(df_list)
+    df.reset_index(inplace=True,drop=True)
+    return df
+
+class ReadingBank(datasets.GeneratorBasedBuilder):
+    """ReadingBank is a benchmark dataset for reading order detection built with weak supervision from WORD documents, which contains 500K document images with a wide range of document types as well as the corresponding reading order information."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "src": datasets.Value("string"),
+                "tgt": datasets.Value("string"),
+                "bleu": datasets.Value("float"),
+                "tgt_index": datasets.Sequence(datasets.Value("int16")),
+                "original_filename": datasets.Value("string"),
+                "filename": datasets.Value("string"),
+                "page_idx": datasets.Value("int16"),
+                "src_layout": datasets.Sequence(datasets.Sequence(datasets.Value("int16"))),
+                "tgt_layout": datasets.Sequence(datasets.Sequence(datasets.Value("int16"))),
+                # 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):
+
+        urls = _URLS["dataset"]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": parse_files(list(Path(f'{data_dir}/train/').glob('*'))),
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": parse_files(list(Path(f'{data_dir}/dev/').glob('*'))),
+                    "split": "dev",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": parse_files(list(Path(f'{data_dir}/test/').glob('*'))),
+                    "split": "test"
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+
+        print('\nCreating dataframes.. please wait..')
+        text_df = get_dataframe(filepath,'text')
+        layout_df = get_dataframe(filepath,'layout')
+        layout_df.rename(columns={'src':'src_layout',
+                                  'tgt':'tgt_layout'},inplace=True)
+
+        df = text_df.merge(layout_df,left_index=True,right_index=True)
+        print('Dataframes created..\n')
+        yield from enumerate(df.to_dict(orient='records'))