First version

README.md

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 ---
-license: cc-by-nc-sa-4.0
+license: cc-by-nc-4.0
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: file
+    dtype: binary
+  - name: labels
+    dtype:
+      class_label:
+        names:
+          '0': letter
+          '1': form
+          '2': email
+          '3': handwritten
+          '4': advertisement
+          '5': scientific report
+          '6': scientific publication
+          '7': specification
+          '8': file folder
+          '9': news article
+          '10': budget
+          '11': invoice
+          '12': presentation
+          '13': questionnaire
+          '14': resume
+          '15': memo
+  splits:
+  - name: test
+    num_bytes: 1349159996
+    num_examples: 991
+  download_size: 0
+  dataset_size: 1349159996
 ---
+
+
+# Dataset Card for RVL-CDIP-N_MultiPage
+
+## Extension
+
+The data loader provides support for loading RVL_CDIP-N in its extended multipage format.
+Big kudos to the original authors (first in CITATION) for collecting the RVL-CDIP-N dataset.
+We stand on the shoulders of giants :)
+
+## Required installation
+
+```bash
+pip3 install pypdf2 pdf2image
+sudo apt-get install poppler-utils
+```

data.tar.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:40cea6613e5b52c6dc617fa645b7f32b1c9566c314497ac6676e72df9ec440c6
+size 1254580948

rvl_cdip_n_mp.py

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+# Copyright 2022 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.
+
+"""RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset"""
+
+
+import os
+import datasets
+from pathlib import Path
+from tqdm import tqdm
+import pdf2image
+
+datasets.logging.set_verbosity_info()
+logger = datasets.logging.get_logger(__name__)
+
+_MODE = "binary"
+
+_CITATION = """\
+@inproceedings{larson2022evaluating,
+	title={Evaluating Out-of-Distribution Performance on Document Image Classifiers},
+	author={Larson, Stefan and Lim, Gordon and Ai, Yutong and Kuang, David and Leach, Kevin},
+	booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
+	year={2022}
+}
+    
+@inproceedings{bdpc,
+    title = {Beyond Document Page Classification},
+    author = {Anonymous},
+    booktitle = {Under Review},
+    year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+The RVL-CDIP-N (Ryerson Vision Lab Complex Document Information Processing) dataset consists of newly gathered documents in 16 classes 
+There are 998 documents for testing purposes. There were 3 documents from the original dataset that could not be retrieved based on the metadata. 
+"""
+
+
+_HOMEPAGE = "https://www.cs.cmu.edu/~aharley/rvl-cdip/"
+_LICENSE = "https://www.industrydocuments.ucsf.edu/help/copyright/"
+
+
+SOURCE = "jordyvl/rvl_cdip_mp"
+_URL = f"https://huggingface.co/datasets/{SOURCE}/resolve/main/data.gz"
+_BACKOFF_folder = "/mnt/lerna/data/RVL-CDIP-NO/RVL-CDIP-N_pdf/data"
+
+_CLASSES = [
+    "letter",
+    "form",
+    "email",
+    "handwritten",
+    "advertisement",
+    "scientific report",
+    "scientific publication",
+    "specification",
+    "file folder",
+    "news article",
+    "budget",
+    "invoice",
+    "presentation",
+    "questionnaire",
+    "resume",
+    "memo",
+]
+
+
+def batched_conversion(pdf_file):
+    info = pdf2image.pdfinfo_from_path(pdf_file, userpw=None, poppler_path=None)
+    maxPages = info["Pages"]
+
+    logger.info(f"{pdf_file} has {str(maxPages)} pages")
+
+    images = []
+
+    for page in range(1, maxPages + 1, 10):
+        images.extend(
+            pdf2image.convert_from_path(pdf_file, dpi=200, first_page=page, last_page=min(page + 10 - 1, maxPages))
+        )
+    return images
+
+
+def open_pdf_binary(pdf_file):
+    with open(pdf_file, "rb") as f:
+        return f.read()
+
+
+class RvlCdipNMp(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+    DEFAULT_CONFIG_NAME = "default"
+
+    def _info(self):
+        if isinstance(self.config.data_dir, str):
+            folder = self.config.data_dir  # contains the folder structure at someone local disk
+        else:
+            if not os.path.exists(_BACKOFF_folder):
+                raise ValueError("No data folder found. Please set data_dir or data_files.")
+            folder = _BACKOFF_folder  # my local path, others should set data_dir or data_files
+        self.config.data_dir = folder
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "file": datasets.Value("binary"),
+                    "labels": datasets.features.ClassLabel(names=_CLASSES),
+                }
+            ),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+            license=_LICENSE,
+            task_templates=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        if self.config.data_dir.endswith(".tar.gz"):
+            archive_path = dl_manager.download(self.config.data_dir)
+            data_files = dl_manager.iter_archive(archive_path)
+        else:
+            data_files = self.config.data_dir
+
+        return [datasets.SplitGenerator(name="test", gen_kwargs={"archive_path": data_files})]
+
+    def _generate_examples(self, archive_path):
+        labels = self.info.features["labels"]
+
+        extensions = {".pdf", ".PDF"}
+
+        for i, path in tqdm(enumerate(Path(archive_path).glob("**/*")), desc=f"{archive_path}"):
+            if path.suffix in extensions:
+                try:
+                    if _MODE == "binary":
+                        images = open_pdf_binary(path)
+                        # batched_conversion(path)
+                    else:
+                        images = path
+
+                    a = dict(
+                        id=path.name,
+                        file=images,
+                        labels=labels.encode_example(path.parent.name.lower()),
+                    )
+                    yield path.name, a
+
+                except Exception as e:
+                    logger.warning(f"{e} failed to parse {i}")